Conversational AI exists on a spectrum—from basic rule-based chatbots to advanced NLP-driven agents. Here's a clear breakdown of the four main types, especially relevant for healthcare and EMR integrations like eCW and Practice Fusion:

1. Rule-Based Chatbots (Decision Tree Logic)

Example: Updox, legacy IVRs

• How it works: Follows pre-set scripts or "if-then" decision trees

• Patient Experience: Click buttons or respond to simple prompts (e.g., “Press 1 for appointments”)

• Pros: Fast to deploy, easy to control

• Cons: Limited understanding, no free-text input

• Ideal For: Reminders, FAQs, basic intake without personalization
  
  

2. Form-Based Conversational Flows

Example: NexHealth, Yosi Health, Phreesia

• How it works: Mimics a conversation but delivers structured forms dynamically

• Patient Experience: Chat-like UI that collects name, DOB, insurance, symptoms

• Pros: Collects structured data; integrates with EMRs

• Cons: Not “smart”; can’t interpret language nuances

• Ideal For: Patient intake, consents, pre-visit screenings
  
  

3. NLP-Powered Conversational AI (Natural Language Processing)

Example: Hyro.ai, Kore.ai

• How it works: Interprets patient intent from natural speech or typed input

• Patient Experience: Can speak or type naturally: “I need to reschedule next week”

• Pros: More flexible, handles many scenarios, multilingual

• Cons: Needs training and context to perform well

• Ideal For: Phone/chat triage, scheduling, live call deflection
  
  

4. Generative AI / LLM-Based Agents

Example: OpenAI ChatGPT (with HIPAA overlay), Microsoft Health Bot + GPT, custom builds

• How it works: Uses large language models to generate human-like responses and summaries

• Patient Experience: Natural, intelligent conversations, even unstructured (e.g., “I’ve had a cough for 3 days”)

• Pros: Highly adaptive, can synthesize complex info

• Cons: Needs guardrails for HIPAA, safety, and accuracy

• Ideal For: Symptom triage, education, pre-charting, mental health screening

Updated list of tools, tailored for compatibility with Practice Fusion and eClinicalWorks (eCW) = The EMRs that I use in Florida.

#1 Conversational AI – Patient Intake

1. phreesia.com = Forms + check-in for PF & eCW

2. updox.com = Messaging + digital intake (PF + eCW)

#2 Conversational AI – Provider/Patient + SOAP Notes

1. nuance.com = Dictation + ambient SOAP (via EHR overlay)

2. tali.ai = AI notetaker; works over PF/eCW (check BAA)

#3 Insurance + Revenue Cycle AI

1. thoughtful.ai = RPA for insurance & billing workflows

2. waystar.com = Claims + eligibility + RCM (PF + eCW)

Note: Other tools may be needed or substituted based on EMR setup.

ex. 

#1 =  Zocdoc.com

#2 = 

Different approaches and categories within the field of Artificial Intelligence:

• Rules-based: This is a traditional AI approach where systems make decisions or solve problems based on a predefined set of rules. These rules are usually created by human experts and are in an "if-then" format. The system follows these rules to process input and produce an output.
  
  

• Machine learning (ML): This is a type of AI that allows computer systems to learn from data without being explicitly programmed. Instead of relying on predefined rules, ML algorithms identify patterns in data and use those patterns to make predictions or decisions. Examples include training a system to recognize images of cats or predict customer behavior based on past purchases.
  
  

• Large Language Model (LLM): This is a type of machine learning model specifically designed to understand and generate human language. LLMs are trained on massive amounts of text data and can perform various natural language processing tasks, such as text generation, translation, summarization, and answering questions. Examples of LLMs include models like GPT-4 or PaLM.

wo general business examples and two healthcare examples for each category:

Rules-based:

• General Business:

  1. Automated Customer Service Chatbot (simple): A chatbot programmed with specific rules to answer frequently asked questions (e.g., "If the user asks about store hours, respond with our hours are 9 am to 6 pm").

  2. Order Processing System: A system that follows predefined rules to process online orders (e.g., "If the order total is over $100, apply a 10% discount").

• Healthcare:

  1. Drug Interaction Checker (basic): A system programmed with rules about known drug interactions to flag potential issues when a patient is prescribed multiple medications. (e.g., "If a patient is prescribed Warfarin and Aspirin, issue a high interaction alert").

  2. Insurance Claim Adjudication (simple): A system that follows rules set by insurance companies to automatically approve or reject certain types of claims based on predefined criteria. (e.g., "If the procedure code is for a routine checkup and the patient is within their annual limit, approve the claim").

Machine Learning:

• General Business:

  1. Product Recommendation System: An e-commerce website using machine learning to suggest products to customers based on their past purchases, Browse history, and the behavior of similar users.

  2. Fraud Detection System: A financial institution using machine learning to identify potentially fraudulent transactions by analyzing patterns in transaction data.

• Healthcare:

  1. Disease Diagnosis: A machine learning model trained on medical images (like X-rays or MRIs) to help radiologists detect signs of diseases like cancer or pneumonia.

  2. Predictive Analytics for Patient Risk: A system that uses machine learning to analyze patient data (e.g., demographics, medical history, vital signs) to predict the likelihood of developing certain conditions or being readmitted to the hospital.

Large Language Model:

• General Business:

  1. Content Creation and Summarization: An LLM used to generate marketing copy, blog posts, or summarize customer reviews for businesses.

  2. Advanced Customer Service Chatbot: A sophisticated chatbot powered by an LLM that can understand and respond to a wide range of customer inquiries in a more natural and conversational way.

• Healthcare:

  1. Extracting Information from Medical Records: An LLM used to analyze unstructured text in electronic health records to extract key information like symptoms, diagnoses, treatments, and medications.

  2. Generating Patient Summaries or Discharge Instructions: An LLM that can automatically create concise summaries of a patient's hospital stay or generate easy-to-understand discharge instructions based on their medical record.

https://www.youtube.com/watch?v=2Qde3hczTUM 

1. Awareness

🔗 Video 1: https://www.youtube.com/watch?v=XCuXg9oPNAU
🔗 Video 2: https://help.gohighlevel.com/support/solutions/articles/48001219997-understanding-attribution-source-ad-reporting-
Goal = Generate visibility via paid traffic & track attribution
KPI = Impressions, CTR, Cost per Lead
SOP = Launch FB/IG/Google Ads → Track with UTMs → Tag source in GHL → Add to Smart List → Slack alert if leads > daily goal
Tools = Meta Ads Manager, Google Ads, GHL UTM tracking, Slack (#ads-performance)

2. Interest

🔗 Video 1: https://www.youtube.com/watch?v=59shTGzlfAk
🔗 Video 2: https://www.youtube.com/watch?v=lmS4lm3zKyc
Goal = Engage & nurture leads through email/SMS
KPI = Email opens, link clicks, engagement score
SOP = Track website/page views → Trigger nurture sequence → Score lead → Tag “Engaged Visitor” → Slack alert for high-score leads
Tools = GHL Workflows, Smart Lists, Email Builder, Slack DM alerts

3. Due Diligence / Discovery

🔗 Video 1: https://www.youtube.com/watch?v=cx2yjsjWi_4
🔗 Video 2: https://www.youtube.com/watch?v=GOlEY8wDP1U
Goal = Book discovery calls & collect business insights
KPI = Call bookings, no-show rate
SOP = Send GHL calendar → Confirmation & reminders auto-send → Kick off “Discovery Workflow” → Slack alert to #sales
Tools = GHL Calendars, Workflows, Forms, Slack (#sales)

4. More Info / Intent

🔗 Video 1: https://www.youtube.com/watch?v=eaviWbK4CLs
🔗 Video 2: https://www.youtube.com/watch?v=MPDbgSLrfo4
Goal = Deliver high-intent resources (pricing, results, proposal)
KPI = Document views, page dwell time
SOP = Trigger email/SMS after pricing page view → Send comparison guide → DM account owner in Slack to follow up
Tools = GHL Trigger Links, Email Builder, Slack (@mention)

5. Yes / No Decision

🔗 Video 1: https://www.youtube.com/watch?v=WKXNB_8lZ3Q
🔗 Video 2: https://www.youtube.com/watch?v=fm2mLfXV0ac
Goal = Capture decision and route next step
KPI = Decision form submissions, conversion rate
SOP = Send decision form → If “Yes” tag & onboard → If “No” → tag and re-engage → Log to Slack w/ emoji vote
Tools = GHL Custom Forms, Automation Triggers, Slack Polls

6. Buy

🔗 Video 1: https://www.youtube.com/live/8L9DPQbyn34
🔗 Video 2: https://www.youtube.com/watch?v=I3foMcl6KT8
Goal = Convert buyer & kickstart onboarding
KPI = Payments processed, client moved to “Active”
SOP = Send contract/invoice → On signature or payment, tag “Client” → Send Welcome sequence → Alert #client-wins
Tools = GHL Payments or Stripe, Contract tool (DocuSign), Workflows, Slack (#client-wins)

7. Onboarding

🔗 Video 1: https://www.youtube.com/watch?v=0rUURrtpLAE
🔗 Video 2: https://www.youtube.com/watch?v=tFe9Y4qll8I
Goal = Setup systems, access, and expectations
KPI = Onboarding complete %, avg onboarding time
SOP = Assign tasks → Send onboarding form & assets checklist → Schedule kickoff call (Thu/Fri) → Update Slack #onboarding
Tools = GHL Pipelines, Intake Forms, Loom, Slack (#onboarding)

8. Adoption

🔗 Video 1: https://www.youtube.com/watch?v=kIdu8gBLqCY
🔗 Video 2: https://www.youtube.com/watch?v=IILU2PLXSQY
Goal = Encourage routine usage of systems
KPI = Workflow completion, system logins, task open rate
SOP = Monitor usage → If inactivity >7 days, send nudge email → Alert Slack #customer-success
Tools = GHL Reporting Dashboard, Triggers, Email reminders, Slack

9. Take Survey (Customer Success Team)

🔗 Video 1: https://help.gohighlevel.com/support/solutions/articles/155000003912-using-the-inline-forms-surveys-element-in-email-campaign
🔗 Video 2: https://rayodaniel.com/gohighlevel-surveys/
Goal = Get customer feedback to improve service
KPI = NPS response %, avg CSAT score
SOP = Send NPS/CSAT form monthly → If score <7, flag in Slack → Route to CSM for recovery
Tools = GHL Surveys, Forms, Email Builder, Slack (#cs)

10. Elevates to Next Level

🔗 Video 1: https://www.youtube.com/watch?v=t24-Q5t3Lmk
🔗 Video 2: https://www.youtube.com/watch?v=aqtKFro8J6w
Goal = Upsell, renew, request testimonial
KPI = Upsell rate, renewal rate, testimonials received
SOP = Tag upgrade/renewal path → Auto-send testimonial ask → Post result in #sales + #leadership
Tools = GHL Tags, Email templates, Slack (#sales, #leadership)

11. Referral

🔗 Video 1: https://www.youtube.com/watch?v=Monno2S7wBk
🔗 Video 2: https://www.reddit.com/r/gohighlevel/comments/1ibmu81/best_way_to_track_leads_and_referral_sources/
Goal = Reward referrals and track new client sources
KPI = Referrals received, rewards distributed
SOP = Send referral form → Auto-tag referral → Trigger $400 reward workflow → Slack ping #referral-team
Tools = GHL Custom Forms, Tags, Email Automation, Slack (#referral-team)

https://www.youtube.com/watch?v=2Qde3hczTUM 

1. Awareness 🔗 Video 1: https://www.youtube.com/watch?v=XCuXg9oPNAU 🔗 Video 2: https://help.gohighlevel.com/support/solutions/articles/48001219997-understanding-attribution-source-ad-reporting- Goal = Generate visibility via paid traffic & track attribution KPI = Impressions, CTR, Cost per Lead SOP = Launch FB/IG/Google Ads → Track with UTMs → Tag source in GHL → Add to Smart List → Slack alert if leads > daily goal Tools = Meta Ads Manager, Google Ads, GHL UTM tracking, Slack (#ads-performance), ChatGPT, Claude, Exa.ai

2. Interest 🔗 Video 1: https://www.youtube.com/watch?v=59shTGzlfAk 🔗 Video 2: https://www.youtube.com/watch?v=lmS4lm3zKyc Goal = Engage & nurture leads through email/SMS KPI = Email opens, link clicks, engagement score SOP = Track website/page views → Trigger nurture sequence → Score lead → Tag “Engaged Visitor” → Slack alert for high-score leads Tools = GHL Workflows, Smart Lists, Email Builder, Slack DM alerts, Instantly.ai, ChatGPT, Claude

3. Due Diligence / Discovery 🔗 Video 1: https://www.youtube.com/watch?v=cx2yjsjWi_4 🔗 Video 2: https://www.youtube.com/watch?v=GOlEY8wDP1U Goal = Book discovery calls & collect business insights KPI = Call bookings, no-show rate SOP = Send GHL calendar → Confirmation & reminders auto-send → Kick off “Discovery Workflow” → Slack alert to #sales Tools = GHL Calendars, Workflows, Forms, Slack (#sales), Hermes AI, Apollo.io, Exa.ai

4. More Info / Intent 🔗 Video 1: https://www.youtube.com/watch?v=eaviWbK4CLs 🔗 Video 2: https://www.youtube.com/watch?v=MPDbgSLrfo4 Goal = Deliver high-intent resources (pricing, results, proposal) KPI = Document views, page dwell time SOP = Trigger email/SMS after pricing page view → Send comparison guide → DM account owner in Slack to follow up Tools = GHL Trigger Links, Email Builder, Slack (@mention), ChatGPT, Claude, DocuSign

5. Yes / No Decision 🔗 Video 1: https://www.youtube.com/watch?v=WKXNB_8lZ3Q 🔗 Video 2: https://www.youtube.com/watch?v=fm2mLfXV0ac Goal = Capture decision and route next step KPI = Decision form submissions, conversion rate SOP = Send decision form → If “Yes” tag & onboard → If “No” → tag and re-engage → Log to Slack w/ emoji vote Tools = GHL Custom Forms, Automation Triggers, Slack Polls, Zapier

6. Buy 🔗 Video 1: https://www.youtube.com/live/8L9DPQbyn34 🔗 Video 2: https://www.youtube.com/watch?v=I3foMcl6KT8 Goal = Convert buyer & kickstart onboarding KPI = Payments processed, client moved to “Active” SOP = Send contract/invoice → On signature or payment, tag “Client” → Send Welcome sequence → Alert #client-wins Tools = GHL Payments, Stripe, DocuSign, Workflows, Slack (#client-wins)

7. Onboarding 🔗 Video 1: https://www.youtube.com/watch?v=0rUURrtpLAE 🔗 Video 2: https://www.youtube.com/watch?v=tFe9Y4qll8I Goal = Setup systems, access, and expectations KPI = Onboarding complete %, avg onboarding time SOP = Assign tasks → Send onboarding form & assets checklist → Schedule kickoff call (Thu/Fri) → Update Slack #onboarding Tools = GHL Pipelines, Intake Forms, Loom, Slack (#onboarding), ChatGPT, Claude

8. Adoption 🔗 Video 1: https://www.youtube.com/watch?v=kIdu8gBLqCY 🔗 Video 2: https://www.youtube.com/watch?v=IILU2PLXSQY Goal = Encourage routine usage of systems KPI = Workflow completion, system logins, task open rate SOP = Monitor usage → If inactivity >7 days, send nudge email → Alert Slack #customer-success Tools = GHL Reporting Dashboard, Triggers, Email reminders, Slack (#customer-success), Claude

9. Take Survey (Customer Success Team) 🔗 Video 1: https://help.gohighlevel.com/support/solutions/articles/155000003912-using-the-inline-forms-surveys-element-in-email-campaign 🔗 Video 2: https://rayodaniel.com/gohighlevel-surveys/ Goal = Get customer feedback to improve service KPI = NPS response %, avg CSAT score SOP = Send NPS/CSAT form monthly → If score <7, flag in Slack → Route to CSM for recovery Tools = GHL Surveys, Forms, Email Builder, Slack (#cs), Claude, ChatGPT

10. Elevates to Next Level 🔗 Video 1: https://www.youtube.com/watch?v=t24-Q5t3Lmk 🔗 Video 2: https://www.youtube.com/watch?v=aqtKFro8J6w Goal = Upsell, renew, request testimonial KPI = Upsell rate, renewal rate, testimonials received SOP = Tag upgrade/renewal path → Auto-send testimonial ask → Post result in #sales + #leadership Tools = GHL Tags, Email templates, Slack (#sales, #leadership), ChatGPT, Zapier

11. Referral 🔗 Video 1: https://www.youtube.com/watch?v=Monno2S7wBk 🔗 Video 2: https://www.reddit.com/r/gohighlevel/comments/1ibmu81/best_way_to_track_leads_and_referral_sources/ Goal = Reward referrals and track new client sources KPI = Referrals received, rewards distributed SOP = Send referral form → Auto-tag referral → Trigger $400 reward workflow → Slack ping #referral-team Tools = GHL Custom Forms, Tags, Email Automation, Slack (#referral-team), Stripe, Zapier

Global Cancer Mortality + Screening Tests:

1. Lung: 1.8M deaths; low-dose CT scans recommended for high-risk smokers aged 50-80.

2. Colorectal: 916K deaths; colonoscopy or fecal occult blood tests starting age 45-50.

3. Liver: 830K deaths; ultrasound and AFP blood tests for high-risk individuals.

4. Stomach: 769K deaths; endoscopy advised in high-incidence regions or family history.

5. Breast: 685K deaths; mammography every 1-2 years starting age 40-50.
   
   

U.S. Cancer Mortality + Screening Tests:

1. Lung: ~127K deaths; annual low-dose CT scans for heavy smokers (ages 50-80).

2. Colorectal: ~53K deaths; colonoscopy every 10 years beginning age 45.

3. Pancreas: ~51K deaths; screening only recommended for very high-risk individuals.

4. Breast: ~43K deaths; mammograms every 1-2 years from age 40-50.

5. Prostate: ~35K deaths; PSA blood test starting age 50, earlier for high-risk men.

https://pubmed.ncbi.nlm.nih.gov/28991558/

https://pubmed.ncbi.nlm.nih.gov/37806742/

high false + , Non-specific.   ... patient anxiety, PET / bx , etc   

RFs 

https://pubmed.ncbi.nlm.nih.gov/36634294/ 

• Supervised Machine Learning: A method where models are trained on labeled data to predict outcomes based on input features, applicable to classification and regression tasks.
  
  

• Unsupervised Learning: Learning from data without labeled outcomes, aiming to find patterns or groupings, often used in clustering or dimensionality reduction.
  
  

• Neural Networks: Algorithms modeled after the human brain, consisting of layers of interconnected nodes (neurons), used for pattern recognition and decision-making tasks.
  
  

• Deep Learning: A subset of machine learning employing multi-layer neural networks to learn hierarchical data representations, excelling in complex tasks like image and speech recognition.
  
  

• Reinforcement Learning: A learning paradigm where an agent interacts with an environment and learns optimal strategies through trial and error, often used in decision-making problems.
  
  

• AI in Healthcare: The application of AI techniques for tasks like disease diagnosis, personalized treatment, drug discovery, and health monitoring through predictive analytics and automated decision-making systems.
  
  

• AI Misconceptions: Common misconceptions include AI’s infallibility, lack of bias, and ability to think independently, which oversimplify AI's capabilities and limitations in real-world applications.
  
  

• Bias in AI: The presence of systemic error in AI models due to biased data, leading to unfair, inaccurate, or discriminatory outcomes, especially problematic in healthcare decision-making.
  
  

• Overfitting: A scenario where a model becomes excessively tailored to training data, capturing noise and irrelevant patterns, reducing its ability to generalize to new data.
  
  

• Suitability of AI in Healthcare: Important questions include evaluating model interpretability, ethical concerns, regulatory compliance, and integration with existing clinical workflows to determine AI’s practical applicability in medical settings.

1. Algorithm= Stepwise procedure for calculations and automated data processing.

2. Artificial Intelligence (AI)= Machines simulating human cognitive functions.

3. Machine Learning (ML)= Algorithms learning patterns from data without explicit programming.

4. Supervised Learning= Training models with labeled data for predictive accuracy.

5. Unsupervised Learning= Identifying hidden patterns in unlabeled data.

6. Reinforcement Learning= Models learning optimal actions via rewards and penalties.

7. Neural Network= Computational networks mimicking biological neuron structures.

8. Deep Learning= Neural networks with multiple hidden layers for complex data representations.

9. Data Mining= Extracting actionable insights from large datasets.

10. Dataset= Structured collection of related data points for analysis.

11. Feature= Individual measurable property or characteristic of data.

12. Label= Known outcome used in supervised learning for training purposes.

13. Classification= Categorizing data into distinct predefined groups.

14. Regression= Predicting continuous outcomes from independent variables.

15. Clustering= Grouping data points based on similarities without predefined categories.

16. Decision Tree= Hierarchical, tree-structured model for decision making.

17. Random Forest= Ensemble of decision trees for robust predictive performance.

18. Support Vector Machine (SVM)= Classifier using hyperplanes to distinguish data categories.

19. Natural Language Processing (NLP)= Algorithms interpreting human language data.

20. Computer Vision= Algorithms interpreting visual information from images or video.

21. Predictive Analytics= Statistical methods forecasting future events from historical data.

22. Training Set= Data subset used to train predictive models.

23. Validation Set= Dataset portion evaluating model performance during training.

24. Test Set= Unseen dataset used to assess final model accuracy.

25. Overfitting= Model excessively tailored to training data, reducing generalization.

26. Underfitting= Model insufficiently capturing underlying data patterns.

27. Hyperparameter= Configurable parameters defining model architecture and learning process.

28. Model Evaluation= Process assessing predictive accuracy and effectiveness.

29. Accuracy= Proportion of correctly predicted data points in a dataset.

30. Precision= Proportion of correctly predicted positive cases among all predicted positives.

31. Recall= Proportion of actual positives accurately identified by the model.

32. F1 Score= Harmonic mean of precision and recall, balancing accuracy metrics.

33. Confusion Matrix= Table summarizing model prediction outcomes versus actual classifications.

34. Bias= Consistent errors resulting from erroneous assumptions in learning algorithms.

35. Variance= Sensitivity of model predictions to fluctuations in training data.

36. Gradient Descent= Optimization algorithm minimizing error functions iteratively.

37. Cost Function= Mathematical function quantifying prediction error of a model.

38. Epoch= Complete pass through entire training dataset in model training.

39. Batch= Subset of training data processed simultaneously in gradient descent.

40. Stochastic Gradient Descent (SGD)= Gradient descent variant using random training subsets.

41. Cross-validation= Method assessing model stability by partitioning data multiple ways.

42. Dimensionality Reduction= Techniques reducing variables while retaining meaningful data.

43. Principal Component Analysis (PCA)= Statistical method simplifying data dimensionality.

44. Feature Engineering= Selecting or transforming data features improving model performance.

45. Anomaly Detection= Identifying rare or unusual data points differing significantly.

46. Ensemble Methods= Combining multiple models to enhance prediction robustness.

47. Bagging= Ensemble technique averaging predictions from independently trained models.

48. Boosting= Ensemble method sequentially training models to correct predecessors’ errors.

49. AdaBoost= Boosting algorithm adjusting weights to minimize classification errors iteratively.

50. XGBoost= Efficient gradient boosting algorithm for enhanced predictive accuracy.

51. K-Nearest Neighbors (KNN)= Classifying data points by similarity to nearest neighbors.

52. Logistic Regression= Predicting categorical outcomes via logistic function modeling.

53. Linear Regression= Predicting continuous outcomes via linear predictor functions.

54. Multilayer Perceptron (MLP)= Neural network consisting of multiple neuron layers.

55. Backpropagation= Algorithm adjusting neural network weights based on error gradients.

56. Activation Function= Nonlinear function determining neuron output activation.

57. Sigmoid Function= Activation function mapping inputs between zero and one.

58. ReLU (Rectified Linear Unit)= Activation function outputting positive inputs directly.

59. Convolutional Neural Network (CNN)= Neural network specializing in spatial data analysis.

60. Recurrent Neural Network (RNN)= Neural network processing sequential data via recurrent connections.

61. Long Short-Term Memory (LSTM)= RNN variant effectively modeling long-term sequential dependencies.

62. Transformer= Neural network architecture efficiently processing sequential data.

63. Transfer Learning= Leveraging pre-trained models for new related tasks.

64. Semi-supervised Learning= Training models using both labeled and unlabeled data.

65. Supervised Labeling= Assigning labels to data points based on prior knowledge.

66. Data Preprocessing= Transforming raw data into suitable format for modeling.

67. Normalization= Scaling data within a specific range for consistency.

68. Standardization= Scaling data to have zero mean and unit variance.

69. Imputation= Estimating and replacing missing values in datasets.

70. Outlier= Data point significantly deviating from overall dataset patterns.

71. Feature Selection= Identifying optimal subset of data features for modeling.

72. Learning Rate= Hyperparameter determining step size during optimization.

73. Regularization= Techniques penalizing complexity to prevent overfitting.

74. L1 Regularization= Regularization method promoting sparsity in models.

75. L2 Regularization= Penalizes squared magnitude of model parameters.

76. Dropout= Technique randomly omitting neurons during training to reduce overfitting.

77. Optimization= Algorithms finding optimal parameters minimizing loss functions.

78. Model Deployment= Implementing trained models into practical applications.

79. Interpretability= Degree to which model predictions are understandable.

80. Explainable AI (XAI)= Methods ensuring model transparency and interpretability.

81. Data Augmentation= Artificially increasing training data diversity.

82. Loss Function= Metric quantifying difference between model predictions and actual values.

83. Perceptron= Simplest artificial neuron model for binary classification.

84. Autoencoder= Neural network reconstructing inputs, used for dimensionality reduction.

85. Generative Adversarial Network (GAN)= Networks competing to generate realistic data samples.

86. Reinforcement Policy= Strategy guiding agent actions in reinforcement learning.

87. Q-Learning= Reinforcement algorithm estimating action-value functions.

88. Markov Decision Process (MDP)= Framework modeling decision-making processes stochastically.

89. Agent= Entity interacting and learning within an environment.

90. Environment= Context in which reinforcement learning agents operate and interact.

91. Reward Function= Signal defining the desirability of agent's actions.

92. Exploration vs. Exploitation= Balance between discovering new actions and optimizing known actions.

93. Bias-Variance Trade-off= Balancing predictive accuracy and model complexity.

94. Data Leakage= Improper exposure of information causing biased model predictions.

95. Pipeline= Automated sequence of data processing steps in modeling workflows.

96. Scalability= Ability of algorithms to effectively handle growing datasets.

97. Model Drift= Performance deterioration due to evolving data distributions.

98. Edge Computing= Processing data near data sources instead of centralized locations.

99. Cloud Computing= Utilizing remote servers to store and process data.

100. Ethical AI= Principles ensuring responsible and unbiased AI practices.

1. Biomedical Informatics=Data science applied to healthcare and biological research.

2. Clinical Decision Support System (CDSS)=Software aiding clinical decision-making.

3. Disease Diagnosis=Identifying diseases based on clinical symptoms and tests.

4. Patient Monitoring=Continuous tracking of patient health status.

5. Electronic Health Record (EHR)=Digital version of patient medical histories.

6. Health Informatics=Managing healthcare information via technology.

7. Predictive Modeling=Forecasting health outcomes using statistical models.

8. Remote Patient Monitoring (RPM)=Health data collection from patients remotely.

9. Wearable Technology=Devices worn to track health metrics.

10. Digital Biomarkers=Digital indicators of health conditions.

11. Medical Imaging=Visual representation of internal body structures.

12. Radiomics=Extracting quantitative data from medical images.

13. Pathomics=Quantitative analysis of pathological imaging data.

14. Bioinformatics=Computational analysis of biological data.

15. Health Data Analytics=Analyzing health data to improve outcomes.

16. Clinical Algorithms=Structured decision-making processes in healthcare.

17. Disease Progression Modeling=Predicting disease advancement using data.

18. Prognostic Models=Models predicting health outcomes and survival.

19. Diagnostic Accuracy=Correctness of medical diagnosis.

20. Sensitivity and Specificity=Measures of diagnostic test accuracy.

21. Area Under Curve (AUC)=Metric evaluating diagnostic model performance.

22. ROC Curve=Graph illustrating model diagnostic capability.

23. Telemedicine=Providing healthcare remotely via technology.

24. IoT in Healthcare=Internet-connected healthcare devices and systems.

25. Real-Time Data Analysis=Immediate analysis of live data.

26. Vital Signs Monitoring=Tracking core physiological measurements.

27. ECG Analysis=Assessment of heart electrical activity.

28. EEG Monitoring=Recording brain electrical activity.

29. Anomaly Detection in Healthcare=Identifying unusual health data patterns.

30. Disease Surveillance=Monitoring disease spread and occurrence.

31. Early Disease Detection=Identifying diseases at initial stages.

32. Precision Medicine=Customized treatment based on individual characteristics.

33. Personalized Health Monitoring=Tailored tracking of individual health parameters.

34. Medical Sensor Data=Data collected from health-monitoring sensors.

35. Smart Health Devices=Intelligent devices enhancing healthcare delivery.

36. Clinical Pathways=Standardized care plans guiding treatment.

37. Digital Twin in Healthcare=Virtual patient models for simulation.

38. Prognosis Prediction=Forecasting patient health outcomes.

39. Clinical Trial Matching=Identifying suitable trials for patient enrollment.

40. Automated Image Analysis=Computerized interpretation of medical images.

41. Quantitative Imaging=Numerical assessment of medical imaging data.

42. Symptom Checker Algorithms=Tools evaluating symptoms for preliminary diagnosis.

43. Risk Stratification=Categorizing patients by disease risk levels.

44. Automated Patient Alerts=System-generated health alerts for clinicians.

45. AI-driven Triage=Automated prioritization of patient care urgency.

46. Clinical NLP=Extracting clinical insights from medical texts.

47. AI-Assisted Radiology=Radiology diagnostics enhanced by artificial intelligence.

48. Computer-Aided Diagnosis (CAD)=AI tools assisting medical diagnosis.

49. Biosignal Processing=Analyzing biological signals for health information.

50. AI-assisted Pathology=Digital pathology aided by artificial intelligence.

51. Functional Imaging Analysis=Assessing physiological functions via imaging.

52. Patient Risk Prediction=Estimating future health risks in patients.

53. Adverse Event Prediction=Anticipating negative healthcare incidents.

54. ICU Monitoring Systems=Continuous patient monitoring in intensive care.

55. AI-powered Endoscopy=Automated analysis during endoscopic procedures.

56. Clinical Surveillance Systems=Monitoring patient data for clinical interventions.

57. Predictive Health Analytics=Forecasting health outcomes using analytics.

58. AI in Dermatology=Automated skin condition diagnosis.

59. Smart Pill Technology=Ingestible sensors for internal monitoring.

60. Continuous Glucose Monitoring (CGM)=Constant tracking of blood sugar levels.

61. AI in Oncology=Cancer diagnosis and treatment supported by AI.

62. Stroke Detection AI=Automated identification of stroke signs.

63. Cardiac Event Prediction=Forecasting heart-related health incidents.

64. AI-Enhanced Echocardiography=AI analysis of heart ultrasound imaging.

65. Predictive ICU Analytics=Forecasting critical events in intensive care.

66. AI for Mental Health Monitoring=Digital tools tracking psychological wellbeing.

67. Digital Therapeutics=Software-based treatments for health conditions.

68. Health Risk Assessment=Evaluating potential health risks.

69. Medication Adherence Monitoring=Tracking patients' medication usage compliance.

70. AI-powered Rehabilitation=Technology-enhanced patient recovery.

71. Smart Hospital Solutions=Integrated intelligent systems optimizing hospital functions.

72. Patient Flow Optimization=Improving healthcare resource and patient management.

73. Clinical Documentation Improvement=Enhancing accuracy of medical records.

74. Virtual Nursing Assistants=AI-driven support for patient care tasks.

75. Robotic Surgery Monitoring=Tracking robotic-assisted surgical procedures.

76. Clinical Outcome Prediction=Estimating patient treatment results.

77. AI-Assisted Neurology=Neurological diagnosis enhanced by artificial intelligence.

78. Sepsis Prediction Algorithms=Early detection of sepsis risks.

79. Automated Lab Test Interpretation=AI interpreting laboratory test results.

80. AI in Respiratory Monitoring=Automated respiratory health tracking.

81. Disease Risk Mapping=Geographical analysis of disease risks.

82. Digital Pathology=Digitized tissue analysis.

83. Genomic Data Integration=Combining genetic data for clinical insights.

84. AI-Enhanced Mammography=AI aiding breast imaging interpretation.

85. Intelligent ICU Systems=Automated intensive care unit management.

86. Patient Safety Monitoring=Systems ensuring patient safety through analytics.

87. AI-driven Patient Engagement=Using AI to improve patient interaction.

88. Predictive Pharmacovigilance=Forecasting medication-related adverse events.

89. AI-assisted Ophthalmology=AI-enhanced eye health diagnosis.

90. Real-Time Health Analytics=Instantaneous processing of health data.

91. AI-driven Population Health=Analytics managing community health.

92. Adaptive Clinical Protocols=Dynamic patient care strategies.

93. AI-enabled Ultrasound=AI-supported interpretation of ultrasound images.

94. Diagnostic Chatbots=Conversational AI for preliminary diagnosis.

95. AI-guided Biopsy=Precision sampling guided by AI.

96. Clinical Text Mining=Extracting clinical knowledge from texts.

97. AI-driven Allergy Detection=Automated identification of allergic conditions.

98. AI-based Pediatric Monitoring=Child health tracking via artificial intelligence.

99. Smart Bed Technology=Beds integrating patient monitoring sensors.

100. Ethical Considerations in Clinical AI=Ensuring responsible AI practices in healthcare.

🔍 Natural Language Processing (NLP)

1. Tokenization
   Breaking raw text into smaller units like words or subwords, allowing AI to analyze structure.
   Example = “Patient has chest pain” → ["Patient", "has", "chest", "pain"]
   
   

2. Stemming
   Simplifying words to their base forms by removing suffixes, often without grammatical awareness.
   Example = "diagnosed", "diagnosing" → "diagnos"
   
   

3. Lemmatization
   Reduces words to their dictionary root using linguistic context, more precise than stemming.
   Example = "diagnoses", "diagnosed" → "diagnose"
   
   

4. Named Entity Recognition (NER)
   Automatically detects and classifies entities (e.g., drugs, diseases, facilities) in clinical text.
   Example = "Prescribed metformin for diabetes" → metformin = Medication, diabetes = Condition
   
   

5. Part-of-Speech Tagging
   Assigns grammatical labels (noun, verb, etc.) to each word, improving syntactic parsing and extraction.
   Example = "Patient has fever" → Patient/Noun, has/Verb, fever/Noun
   
   

6. Dependency Parsing
   Analyzes sentence grammar by mapping how words relate hierarchically and functionally.
   Example = "Patient takes ibuprofen" → "takes" governs both subject and object
   
   

7. Co-reference Resolution
   Links pronouns or repeated mentions to the same entity for accurate context interpretation.
   Example = "Dr. Smith examined her. She is stable." → her = She = the patient
   
   

8. Semantic Role Labeling
   Identifies the role of each phrase in a sentence (agent, action, recipient).
   Example = "Nurse administered injection" → Nurse = Agent, Injection = Object
   
   

9. Entity Linking
   Matches detected terms to structured database identifiers for standardization.
   Example = "Tylenol" → linked to its RxNorm ID
   
   

10. Word Embeddings
    Transforms words into dense vectors capturing meaning, useful for clustering and semantic analysis.
    Example = "pain" and "ache" → vectors are closely aligned
    
    

11. Contextual Embeddings
    Represent words based on surrounding text, allowing for meaning variation by usage.
    Example = "cold" in "cold weather" vs. "common cold" → different vectors
    
    

12. Transformer Architecture
    A deep learning model that processes all words in parallel using attention mechanisms.
    Example = Used in BERT, GPT, and BioBERT models
    
    

13. Pretraining and Fine-tuning
    Model learns general language features first, then adapts to a specific healthcare task.
    Example = BioBERT pretrained on PubMed → fine-tuned for diagnosis coding
    
    

14. BioBERT / ClinicalBERT
    Domain-specific transformer models trained on biomedical or clinical text for better accuracy.
    Example = Better recognizes terms like "COPD" or "HbA1c"
    
    

15. Multilingual NLP
    NLP systems trained across multiple languages to serve diverse populations and datasets.
    Example = Recognizes "dolor de cabeza" = "headache"
    
    

16. Zero-shot Classification
    Performs classification on unseen labels using language inference without retraining.
    Example = Identify cancer type from symptoms, no cancer-specific training needed
    
    

17. Few-shot Learning
    Learns new tasks from only a few labeled examples using general knowledge.
    Example = Chatbot classifies a new query with just 5 labeled inputs
    
    

18. Prompt Engineering
    Designing effective instructions to guide AI model outputs in a consistent, accurate way.
    Example = Prompt: “Summarize patient note in 3 sentences”
    
    

19. Conversational AI
    AI agents designed to carry on natural, multi-turn conversations with users or patients.
    Example = Virtual nurse answering symptom questions
    
    

20. Dialogue State Tracking
    Maintains context and user intent across multiple exchanges in a conversation.
    Example = Tracks if a patient already asked for a refill
    
    

📊 Data Analytics & Informatics

21. Interoperability
    The ability of healthcare systems to exchange, interpret, and use data cohesively across platforms.
    Example = EHR data shared between clinics via API
    
    

22. FHIR
    A standardized framework (Fast Healthcare Interoperability Resources) for structured clinical data sharing.
    Example = Patient, Condition, and Observation are standard FHIR resources
    
    

23. De-identification
    The process of stripping data of personal identifiers to protect privacy under HIPAA.
    Example = Replace “John, 67” → “[Name], [Age]”
    
    

24. Data Harmonization
    Standardizing varied data formats or terms into a unified, analyzable structure.
    Example = Harmonize “BP” and “Blood Pressure” into one field
    
    

25. Clinical Ontologies
    Structured vocabularies that define relationships between medical concepts for semantic consistency.
    Example = SNOMED links "stroke" and "cerebrovascular accident"
    
    

26. Data Imputation
    Filling in missing values using statistics or models to avoid biased analyses.
    Example = Estimate missing BMI from height and weight
    
    

27. Time Series Forecasting
    Using past trends to predict future values in time-stamped clinical data.
    Example = Predict ER visits next month based on past year’s data
    
    

28. Survival Analysis
    Analyzes time until an event (e.g., death, relapse) with censored data.
    Example = Estimate 2-year survival after lung cancer diagnosis
    
    

29. Cohort Segmentation
    Grouping patient populations based on shared demographics or clinical characteristics.
    Example = Grouping diabetics by age and A1c control
    
    

30. Dimensionality Reduction
    Reduces large datasets to fewer variables while preserving essential structure.
    Example = PCA simplifies 100 lab values into 3 components
    
    

31. Clustering
    Unsupervised grouping of similar data points for pattern discovery.
    Example = Identify patient groups with similar symptom patterns
    
    

32. Classification Models
    Predict categorical outcomes based on input data.
    Example = Predict “benign” vs. “malignant” tumor
    
    

33. Regression Analysis
    Predicts continuous outcomes from numeric or categorical inputs.
    Example = Estimate cholesterol level from weight, diet, and genetics
    
    

34. Outlier Detection
    Spots data points that deviate significantly from expected patterns.
    Example = Lab value 10x higher than normal range is flagged
    
    

35. Data Drift Monitoring
    Detects shifts in model input patterns that may degrade performance.
    Example = COVID-related symptom terms differ from training data
    
    

36. Model Calibration
    Aligns predicted probabilities with actual event rates to improve decision trust.
    Example = If model says 90% risk, 9 out of 10 should be true
    
    

37. Statistical Power
    Probability that a test correctly detects a real effect.
    Example = 80% power means 4 out of 5 times it detects true differences
    
    

38. Missing Data Mechanisms
    Classifies why data is absent—randomly, systematically, or related to outcomes.
    Example = MCAR vs. MAR vs. MNAR in EHR gaps
    
    

39. Longitudinal Data Modeling
    Analyzing how values change for individuals over time.
    Example = Charting A1c over 12 months for diabetics
    
    

40. Real-world Evidence (RWE)
    Insights from actual patient data outside of controlled trials.
    Example = Using EHR and claims data to evaluate treatment outcomes
    
    

🧠 AI Agent Development & Governance

41. Autonomous AI Agents
    AI tools that independently complete tasks without real-time human input.
    Example = An intake bot that asks, records, and routes patient complaints
    
    

42. Workflow Orchestration
    Coordinating automated steps in a clinical or business process using rules or triggers.
    Example = Auto-notify nurse, then physician, when high BP detected
    
    

43. Task Decomposition
    Breaking down large tasks into smaller, automatable steps for AI agents.
    Example = Step 1: Read note → Step 2: extract vitals → Step 3: summarize
    
    

44. Explainable AI (XAI)
    Making AI decisions interpretable and transparent to clinicians or regulators.
    Example = “Cancer risk = 0.87” because of weight, age, and CT finding
    
    

45. Reinforcement Learning
    AI learns from trial and error using rewards to optimize future actions.
    Example = Chatbot improves scheduling efficiency by user feedback
    
    

46. Active Learning
    System selectively queries for labels on uncertain samples to improve model performance.
    Example = Model asks radiologist to label difficult tumor images
    
    

47. Human-in-the-Loop Systems
    AI workflows where humans review, approve, or override decisions.
    Example = Physician validates diagnosis suggested by AI before action
    
    

48. Bias and Fairness Audits
    Evaluates AI models for disparities across race, gender, age, etc.
    Example = Ensure equal accuracy of risk prediction for Black and White patients
    
    

49. Ethical AI Frameworks
    Guidelines to ensure responsible and equitable use of AI systems in healthcare.
    Example = Follow WHO, IEEE, or AMA ethics guidelines for AI deployment
    
    

50. Regulatory Compliance
    Ensuring AI systems follow legal requirements like HIPAA, GDPR, and FDA guidelines.
    Example = PHI encryption, audit logs, and IRB approval for clinical tools

MIDWAY - TAMPA - 

FLOWER

https://www.trulieve.com/content/dam/trulieve/en/lab-reports/79086_0007184906.pdf?download=false

VAPE

CONCENTRATES

TINCTURES

TABLETS/CAPSULES

EDIBLES

TOPICALS

https://www.trulieve.com/content/dam/trulieve/en/lab-reports/63849_0007183856.pdf?download=false 

Dr Newton's General Guidelines | Each patient is unique. 

These are only examples for education purposes only. 

SEE YOUR DOCTOR FOR ADVICE. 

FOR FLORIDA QUALIFIED CONDITIONS VISIT OMMU

Fast Acting Routes Of Administration = Inhalation Dosing (Every 5 Minutes as Needed)

• Very Sensitive: Start with 1 inhalation every 5-10 minutes as needed.

• New to Cannabis: Use 1–2 inhalations every 5-10 minutes as needed.

• Experienced: Take 2–4 inhalations every 5-10 minutes as needed.

• Very Experienced: Use concentrates like wax or resin as needed. Consider balanced approach to moderate intake from a single route (ex. Taking oral and inhalation routes may help reduce inhalation alone) 

Long Lasting Routes of Administration = Oral Dosing (Sublingual, Capsule, or Edible Every 4 Hours)

• Very Sensitive: Start with 2.5 mg every 4 hours.

• New to Cannabis: Start with 5 mg every 4 hours.

• Experienced: Use 10 mg every 4 hours.

• Very Experienced: Take 10–20 mg every 4 hours based on tolerance.

SEE YOUR DOCTOR FOR ADVICE. 

EXAMPLES OF STARTING DOSES 

Cancer – Oral – Start 5 mg THC BID for pain/appetite.  --> RSO BUCCAL MUCOSA + ORAL 

Epilepsy / Seizure Disorders– Oral CBD – Start 5 mg/kg/day divided BID.   -->  REDUCED SEIZURES. NEUROLOGIST COLLABORATION WHEN STARTING AND WEANING ON OTHER AEDs. 

Glaucoma – Inhalation – Start 2.5 mg THC QID.   --> TRANSIENT REDUCTIONS  IN  IOP /  CONTINUE SPECIALIST F/U.

HIV/AIDS – Edible – Start 2.5 mg THC BID for appetite.   --> IMPROVED APPETITE  / MONITOR NUTRITION AND WEIGHT.

PTSD – Sublingual – Start 2.5 mg THC at bedtime.  --> IMPROVEMENT IN SLEEP/REDUCED ANXIETY  & PANIC ATTACKS  /  IMPROVE  WELL-BEING. REDUCE STIMULANT INTAKE.

ALS – Oral – Start 2.5 mg THC BID for spasticity.  --> NEUROPROTECTIVE AGENT. GOAL = REDUCE  DISEASE PROGRESSION BY INCLUDING COMPREHENSIVE REGIMENS. 

Crohn's – Oral – Start 5 mg THC daily, titrate PRN. --> REDUCE GI INFLAMMATION. REDUCE NEED FOR IMMUNOSUPPRESSIVE DRUGS AND POTENTIALLY REDUCE THE NEED FOR SURGERY.

Parkinson’s – Sublingual – Start 1.25 mg THC BID. --> RSO SYRINGE. NEUROPROTECTIVE AGENT. REDUCE 

MS – Oral – Start 2.5-5 mg 1:1 CBD:THC BID for spasm/pain.  --> NEUROPROTECTIVE AGENT. REDUCE SPASTICITY / IMPROVE RANGE OF  MOTION /  IMPROVE SLEEP  AND MOOD

Chronic Pain – Inhalation – Start 2.5 mg THC TID.  --> VARIES BY CONDITION.  REDUCE PAIN BY 30% OR MORE. IMPROVE ACTIVITY. REDUCE  INFLAMMATION / MUSCLE SPASMS / IMPROVE SLEEP.

Terminal Illness – Edible – Start 2.5 mg THC QID. --> VARIES BY CONDITION. REDUCE ANXIETY/DEPRESSION/PTSD PAIN/INSOMNIA/ N/V, ET AL . 

SIMILAR CONDITIONS: 

Anxiety (if approved) IS SIMILAR TO PTSD. 

Insomnia IS SIMILAR TO PAIN, PTSD, AND NEUROLOGIC CONDITION.  OFTEN CAUSED BY A DISRUPTION IN NEURON / NEUROTRANSMITTER FUNCTION. 

Florida Statute §381.986 – Medical Use of Marijuana (2023 Summary)

Florida Statute §381.986 – Medical Use of Marijuana (2023 Summary)

(1) Definitions
Clarifies terms like “low-THC cannabis,” “medical marijuana treatment center (MMTC),” “caregiver,” and “qualified physician.” Terms determine scope and compliance boundaries.

(2) Qualifying Conditions
Lists conditions like cancer, epilepsy, PTSD, HIV/AIDS, and chronic pain. Use must be consistent with physician certification.

(3) Physician Certification
Qualified physicians may issue certifications for MMJ. Must include diagnosis, dosage, and administration route. Certifications must be documented in the registry.

(4) Physician Education Requirements
Physicians must complete a 2-hour DOH-approved course to qualify for recommending MMJ. Required every time license is renewed.

(5) Identification Cards
Patients and caregivers must obtain DOH-issued ID cards. ID cards must be valid and presented when receiving products.

(6) Medical Marijuana Use Registry
Central database that tracks patient orders, physician certifications, and dispensing activity. Entries must be current and accurate.

(7) Supply and Dosage Limits
Patients may receive a maximum 70-day supply, with 3 refills. Smokable marijuana limited to 2.5 ounces per 35 days unless exception granted.

(8) Prohibited Conduct
Public use, transferring MMJ, or use while driving or working is banned. Noncompliance may result in penalties or loss of access.

(9) Packaging and Labeling
All products must be sealed, tamper-evident, child-resistant, and clearly labeled with THC/CBD content, batch number, and health warnings.

(10) Transportation
Only intrastate transport is allowed. Vehicles must be secure. All MMJ transported must be logged and accompanied by documentation.

(11) Testing Requirements
All MMJ must be tested by certified labs for potency, residual solvents, heavy metals, and microbiological contaminants. Test results must be accessible.

(12) MMTC Operations and Licensing
MMTCs must handle all stages from cultivation to dispensing. Facilities are subject to inspections and must comply with operational and hygiene standards.

(13) Dispensing Facilities
Facilities must be physically secure, ADA-compliant, and equipped with cameras, vaults, and restricted access zones. Products dispensed must match registry records.

(14) Background Screening
Employees must be at least 21 years old and pass a Level 2 background check. Records must be maintained and available for review.

(15) Penalties and Disciplinary Action
DOH can suspend, fine, or revoke licenses for violations, mislabeling, diversion, or recordkeeping failures. Violations must be reported.

(16) Rulemaking Authority
DOH is authorized to issue rules and forms necessary for implementation. Facilities must stay updated with current rules under Chapter 64-4, FAC.

(17) Confidentiality
All MMJ patient and caregiver data are protected and confidential under HIPAA. Security measures must be in place to prevent unauthorized access.

(18) Edibles Regulations
Edibles must not resemble candy, must be safe for consumption, shelf-stable, clearly labeled, and manufactured in compliance with food code requirements.

(19) State Preemption
State law overrides local bans on MMJ. Local governments may only regulate time, place, and manner of MMTC operations. 

1. Cancer (FOR MORE SEE THE CANCER SECTION) 

Study 1

Citation: Bar-Sela, G., Zalman, D., Bergman, R., & Visel, B. (2019). Cannabis consumption in palliative care patients: A prospective observational study. Supportive Care in Cancer, 27(5), 1759–1766. https://doi.org/10.1007/s00520-018-4441-y

Result: Significant reductions reported in pain intensity, nausea, anxiety, depression, and overall distress scores after 6 months of cannabis treatment in palliative cancer patients.

Conclusion: Medical cannabis treatment may significantly improve symptoms and overall quality of life for palliative cancer patients.

Study 2

Citation: Tramer, M. R., Carroll, D., Campbell, F. A., Reynolds, D. J. M., Moore, R. A., & McQuay, H. J. (2001). Cannabinoids for control of chemotherapy induced nausea and vomiting: quantitative systematic review. BMJ, 323(7303), 16–21. https://doi.org/10.1136/bmj.323.7303.16  

Result: Systematic review found cannabinoids were more effective than conventional antiemetics (prochlorperazine, metoclopramide, etc.) in controlling chemotherapy-induced nausea and vomiting in analyzed trials.

Conclusion: Cannabinoids show superior efficacy compared to some older antiemetic drugs for chemotherapy-induced nausea and vomiting, but side effects were noted.

2. Epilepsy

Study 1

Citation: Devinsky, O., Cross, J. H., Laux, L., Marsh, E., Miller, I., Nabbout, R., Scheffer, I. E., Thiele, E. A., & Wright, S. (2017). Trial of Cannabidiol for Drug-Resistant Seizures in the Dravet Syndrome. The New England Journal of Medicine, 376(21), 2011–2020. https://doi.org/10.1056/NEJMoa1611618  

Result: Patients with Dravet syndrome receiving cannabidiol (CBD) experienced a significantly greater median reduction in convulsive seizure frequency (38.9%) compared to placebo (13.3%).

Conclusion: Cannabidiol is effective in reducing the frequency of convulsive seizures in patients with Dravet syndrome compared to placebo.

Study 2

Citation: Thiele, E. A., Marsh, E. D., French, J. A., Mazurkiewicz-Bełdzińska, M., Benbadis, S. R., Joshi, C., Lyons, P. D., Taylor, A., Roberts, C., & Sommerville, K. (2018). Cannabidiol in patients with seizures associated with Lennox-Gastaut syndrome (GWPCARE4): a randomised, double-blind, placebo-controlled phase 3 trial. The Lancet, 391(10125), 1085–1096. https://doi.org/10.1016/S0140-6736(18)30136-3  

Result: Patients receiving CBD (20 mg/kg/day) had a median reduction in drop seizure frequency of 43.9%, significantly greater than the 21.8% reduction in the placebo group.

Conclusion: Add-on treatment with cannabidiol resulted in a greater reduction in drop seizure frequency than placebo among patients with Lennox-Gastaut syndrome.

3. Glaucoma

Study 1

Citation: Merritt, J. C., Crawford, W. J., Alexander, P. C., Anduze, A. L., & Gelbart, S. S. (1980). Effect of marihuana on intraocular and blood pressure in glaucoma. Ophthalmology, 87(3), 222–228. https://doi.org/10.1016/s0161-6420(80)35251-x  

Result: Inhalation of marijuana significantly lowered intraocular pressure (IOP) in patients with primary open-angle glaucoma.

Conclusion: Marijuana smoking causes a significant reduction in IOP in glaucoma patients, though effects are relatively short-lived.

Study 2

Citation: Tomida, I., Azuara-Blanco, A., House, H., Flint, M., Pertwee, R. G., & Robson, P. J. (2006). Effect of sublingual application of cannabinoids on intraocular pressure: a pilot study. British Journal of Ophthalmology, 90(7), 851–853. https://doi.org/10.1136/bjo.2005.086414  

Result: Sublingual delta-9-THC (5mg) significantly reduced IOP 2 hours post-administration, while sublingual CBD (20mg) had no effect, and a higher CBD dose (40mg) transiently increased IOP.

Conclusion: Low-dose sublingual THC can transiently lower IOP, whereas CBD does not appear to lower IOP and may even increase it at higher doses.

4. HIV (Human Immunodeficiency Virus) / AIDS (Acquired Immune Deficiency Syndrome)

Study 1

Citation: Abrams, D. I., Jay, C. A., Shade, S. B., Vizoso, H., Reda, H., Press, S., Kelly, M. E., Rowbotham, M. C., & Petersen, K. L. (2007). Cannabis in painful HIV-associated sensory neuropathy: A randomized placebo-controlled trial. Neurology, 68(7), 515–521. https://doi.org/10.1212/01.wnl.0000253187.66183.9c  

Result: Patients smoking cannabis experienced greater pain relief (median 34% reduction) for HIV-associated sensory neuropathy compared to those smoking placebo cigarettes (median 17% reduction).

Conclusion: Smoked cannabis was well tolerated and effectively relieved chronic neuropathic pain from HIV-associated sensory neuropathy.  

Study 2

Citation: Ellis, R. J., Toperoff, W., Vaida, F., van den Brande, G., Gonzales, J., Gouaux, B., Bentley, H., & Atkinson, J. H. (2009). Smoked medicinal cannabis for neuropathic pain in HIV: a randomized, crossover clinical trial. Neuropsychopharmacology, 34(3), 672–680. https://doi.org/10.1038/npp.2008.120  

Result: Smoked cannabis significantly reduced daily neuropathic pain intensity compared to placebo in HIV patients (46% achieved >30% pain relief with cannabis vs. 18% with placebo).

Conclusion: Smoked cannabis is a potentially effective option for treating neuropathic pain in HIV infection.

5. Amyotrophic Lateral Sclerosis (ALS)

Study 1

Citation: Amtmann, D., Weydt, P., Carter, G. T., & Weiss, M. D. (2004). Survey of cannabis use in patients with amyotrophic lateral sclerosis. The American Journal of Hospice & Palliative Care, 21(2), 95–104. https://doi.org/10.1177/104990910402100206  

Result: In a survey, ALS patients reported using cannabis for symptom relief, primarily for appetite loss, depression, pain, spasticity, and drooling, with moderate perceived effectiveness.

Conclusion: ALS patients use cannabis to manage various symptoms, suggesting potential benefits warranting further clinical investigation.

Study 2

Citation: Riva, N., Mora, G., Sorarù, G., Lunetta, C., Ferraro, O. E., Falzone, Y., Leocani, L., Fazio, R., & Filippi, M. (2019). Safety and efficacy of nabiximols on spasticity symptoms in patients with motor neuron disease (CANALS): a randomised, double-blind, placebo-controlled trial. The Lancet Neurology, 18(2), 155–164. https://doi.org/10.1016/S1474-4422(18)30406-X

Result: Nabiximols (THC:CBD oromucosal spray) showed a statistically significant improvement in spasticity scores (NRS) compared to placebo in patients with motor neuron disease (including ALS).

Conclusion: Nabiximols may be a useful treatment option for managing spasticity symptoms in patients with motor neuron disease.

6. Crohn's Disease

Study 1

Citation: Naftali, T., Bar-Lev Schleider, L., Dotan, I., Lansky, E. P., Sklerovsky Benjaminov, F., & Konikoff, F. M. (2013). Cannabis induces a clinical response in patients with Crohn's disease: a prospective placebo-controlled study. Clinical Gastroenterology and Hepatology, 11(10), 1276–1280.e1. https://doi.org/10.1016/j.cgh.2013.04.034  

Result: Complete remission was achieved by 5 of 11 subjects smoking cannabis cigarettes (THC-rich), compared to 1 of 10 on placebo. A clinical response (>100 point reduction in CDAI) occurred in 10 of 11 cannabis subjects vs. 4 of 10 placebo subjects.

Conclusion: Short-term (8 weeks) use of THC-rich cannabis produced significant clinical benefits in patients with Crohn's disease, although it did not induce endoscopic remission.

Study 2

Citation: Naftali, T., Mechulam, R., Marii, A., Gabay, G., Stein, A., Bronshtain, M., Laish, I., Benjaminov, F., & Konikoff, F. M. (2017). Low-Dose Cannabidiol Is Safe but Not Effective in the Treatment for Crohn's Disease, a Randomized Controlled Trial. Digestive Diseases and Sciences, 62(6), 1615–1620. https://doi.org/10.1007/s10620-017-4540-z (Note: This study title indicates lack of effectiveness for primary outcome, but it still informs the research)  

Result: While low-dose CBD did not significantly improve Crohn's Disease Activity Index (CDAI) scores compared to placebo, patients receiving CBD reported improvements in quality of life.

Conclusion: Low-dose CBD alone was safe but did not demonstrate effectiveness in reducing Crohn's disease activity scores, though subjective quality of life improvements were noted.

7. Parkinson's Disease

Study 1

Citation: Chagas, M. H. N., Eckeli, A. L., Zuardi, A. W., Pena-Pereira, M. A., Sobreira-Neto, M. A., Sobreira, E. T., Camilo, M. R., Bergamaschi, M. M., Schenck, C. H., Hallak, J. E. C., Tumas, V., & Crippa, J. A. S. (2014). Cannabidiol can improve complex sleep-related behaviours associated with rapid eye movement sleep behaviour disorder in Parkinson's disease patients: a case series. Journal of Clinical Pharmacy and Therapeutics, 39(5), 564–566. https://doi.org/10.1111/jcpt.12179  

Result: CBD administration promptly reduced the frequency of REM sleep behavior disorder (RBD) events in four Parkinson's disease patients without side effects.

Conclusion: Cannabidiol shows potential for controlling the symptoms of RBD in patients with Parkinson's disease.

Study 2

Citation: Lotan, I., Treves, T. A., Roditi, Y., & Djaldetti, R. (2014). Cannabis (medical marijuana) treatment for motor and non-motor symptoms of Parkinson disease: an open-label study. Clinical Neuropharmacology, 37(2), 41–44. https://doi.org/10.1097/WNF.0000000000000016  

Result: Significant improvement in motor scores (UPDRS), tremor, rigidity, bradykinesia, sleep, and pain scores were observed 30 minutes after cannabis consumption in Parkinson's patients.

Conclusion: Medical cannabis (smoked) demonstrated a significant improvement in motor and non-motor symptoms among patients with Parkinson's disease in this short-term observational study.

8. Multiple Sclerosis (MS)

Study 1

Citation: Zajicek, J. P., Sanders, H. P., Wright, D. E., Vickery, P. J., Ingram, W. M., Reilly, S. M., Nunn, A. J., Teare, L. J., Fox, P. J., & Thompson, A. J. (2003). Cannabinoids for treatment of spasticity and other symptoms related to multiple sclerosis (CAMS study): multicentre randomised placebo-controlled trial. The Lancet, 362(9395), 1517–1526. https://doi.org/10.1016/s0140-6736(03)14738-1  

Result: While objective spasticity measures didn't significantly differ, patients taking cannabis extract or THC reported subjective improvements in spasticity and pain compared to placebo.

Conclusion: Cannabinoids may be clinically useful for treating MS symptoms like spasticity and pain, primarily based on patient-reported outcomes.

Study 2

Citation: Novotna, A., Mares, J., Ratcliffe, S., Novakova, I., Vachova, M., Zapletalova, O., Gasperini, C., Pozzilli, C., Cefaro, L., Comi, G., Rossi, P., Ambler, Z., Stelmasiak, Z., & Unger, S. (2011). A randomized, double-blind, placebo-controlled, parallel-group, enriched-design study of nabiximols* (Sativex® ), as add-on therapy, in subjects with refractory spasticity caused by multiple sclerosis. European Journal of Neurology, 18(9), 1122–1131. https://doi.org/10.1111/j.1468-1331.2010.03328.x  

Result: Patients with refractory MS spasticity who initially responded to nabiximols (Sativex) showed significantly greater improvement in spasticity scores during the randomized phase compared to those switched to placebo.

Conclusion: Nabiximols (THC:CBD spray) is an effective add-on treatment for reducing spasticity in MS patients who haven't responded adequately to other therapies.

Post-Traumatic Stress Disorder (PTSD)

Study 1

• Citation: Blessing, E. M., Steenkamp, M. M., Manzanares, J., & Marmar, C. R. (2015). Cannabidiol as a Potential Treatment for Anxiety Disorders. Neurotherapeutics, 12(4), 825–836. https://doi.org/10.1007/s13311-015-0387-1  

• Result: This review examines preclinical and clinical evidence suggesting CBD's potential in reducing anxiety-related symptoms, which overlap with PTSD symptoms.

• Conclusion: Cannabidiol (CBD) shows promise as a therapeutic option for anxiety disorders, and therefore potentially PTSD, due to its effects on the endocannabinoid system.

Study 2

• Citation: Elms, N. J., Shannon, S., Hughes, S., & Lewis, N. (2019). Cannabidiol in the Treatment of Post-Traumatic Stress Disorder: A Case Series. Journal of Alternative and Complementary Medicine, 25(4), 392–397. https://doi.org/10.1089/acm.2018.0437  

• Result: This case series showed that CBD, in conjunction with routine psychiatric treatment, was associated with a reduction in PTSD symptoms in adult outpatients.

• Conclusion: CBD may be a beneficial adjunct therapy for PTSD, demonstrating a reduction in symptoms when added to traditional treatment.

PTSD in Veterans

Study 1

• Citation: Jetcheva, V., & Tashkin, D. P. (2019). Effects of marijuana on neurocognitive function and brain structure in veterans with posttraumatic stress disorder. Journal of Psychoactive Drugs, 51(1), 1–13. https://doi.org/10.1080/02791072.2018.1542125

• Result: This study examined the effects of marijuana use on neurocognitive function and brain structure in veterans with PTSD. While some veterans reported symptom relief, the study also revealed potential negative impacts on certain cognitive functions.

• Conclusion: The relationship between marijuana use and PTSD symptoms in veterans is complex and requires careful consideration of potential risks and benefits.

Study 2

• Citation: Fraser, G. A. (2009). Use of a synthetic cannabinoid in a veteran with posttraumatic stress disorder: a case report. The Canadian Journal of Clinical Pharmacology, 16(1), e16–e19. https://www.cjcp.ca/index.php/cjcp/article/view/1004

• Result: This case report described the use of a synthetic cannabinoid (nabilone) in a veteran with PTSD, showing a positive impact on nightmares and sleep disturbances.

• Conclusion: Synthetic cannabinoids may offer some benefit in managing specific PTSD symptoms, like sleep disturbances, in veterans.

1. Curcumin (from Turmeric)

• Dose: 500–2,000 mg/day (enhanced with black pepper or lipid carriers)
  
  

• Organ Affinity: Liver, colon, breast
  
  

• Mechanism: Anti-inflammatory; inhibits tumor growth pathways
  
  

• Study: Phase I dose-escalation trial with daily doses of 500–2,000 mg
  
  

• URL: https://www.cancer.gov/about-cancer/treatment/cam/hp/curcumin-pdqCancer.gov
  
  

2. Sulforaphane (from Broccoli Sprouts)

• Dose: 20–40 mg/day (approx. 100–200 μmol)
  
  

• Organ Affinity: Prostate, colon, lungs
  
  

• Mechanism: Activates detoxification enzymes; induces apoptosis
  
  

• Study: Phase II trial demonstrating reduced Ki-67 index in lung tissue
  
  

• URL: https://aacrjournals.org/cancerpreventionresearch/article/doi/10.1158/1940-6207.CAPR-24-0386/754246Nature+5PMC+5Personalized Nutrition for Better Health+5AACR Journals+1AACR Journals+1
  
  

3. Annonacin (from Soursop)

• Dose: 1–10 mg/day (caution: potential neurotoxicity at higher doses)
  
  

• Organ Affinity: Breast, pancreas, liver
  
  

• Mechanism: Disrupts mitochondrial function in cancer cells
  
  

• Study: Induced cell cycle arrest and apoptosis in cancer cell lines
  
  

• URL: https://www.sciencedirect.com/science/article/abs/pii/S0378874111005496ScienceDirect+1ScienceDirect+1
  
  

4. EGCG (Epigallocatechin Gallate from Green Tea)

• Dose: 200–400 mg/day
  
  

• Organ Affinity: Bladder, breast, skin
  
  

• Mechanism: Inhibits angiogenesis; antioxidant properties
  
  

• Study: Phase I pharmacokinetic study with doses up to 800 mg
  
  

• URL: https://pubmed.ncbi.nlm.nih.gov/11205489/PMC+6The Guardian+6Spandidos Publications+6PubMed
  
  

5. Allicin (from Garlic)

• Dose: 2–5 mg/day (best obtained from fresh garlic or stabilized extracts)
  
  

• Organ Affinity: Stomach, colon, blood
  
  

• Mechanism: Protects DNA; induces apoptosis
  
  

• Study: Clinical trial administering allicin to cancer patients
  
  

• URL: https://www.mdpi.com/1420-3049/29/6/1320Compass Oncology+1Verywell Health+1MDPI
  
  

6. Berberine (from Goldenseal, Barberry)

• Dose: 500–1,500 mg/day
  
  

• Organ Affinity: Colon, liver, pancreas
  
  

• Mechanism: Activates AMPK; exhibits antiproliferative effects
  
  

• Study: Clinical studies using doses ranging from 600 to 1,500 mg/day
  
  

• URL: https://ascopost.com/issues/july-25-2023/berberine/Healthline+30Healthline+30Personalized Nutrition for Better Health+30HealthHealth+2The ASCO Post+2The ASCO Post+2
  
  

7. Quercetin (Found in Apples, Onions, Capers)

• Dose: 250–1,000 mg/day
  
  

• Organ Affinity: Lungs, prostate, brain
  
  

• Mechanism: Antioxidant; induces cell cycle arrest
  
  

• Study: Clinical trial assessing antioxidant effects of 250 mg daily
  
  

• URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10674654/Personalized Nutrition for Better HealthThe ASCO PostPMC+2PMC+2PMC+2
  
  

8. Resveratrol (from Grapes, Red Wine, Supplements)

• Dose: 100–500 mg/day
  
  

• Organ Affinity: Heart, breast, brain
  
  

• Mechanism: Antioxidant; modulates inflammation and gene expression
  
  

• Study: Review of clinical use and efficacy
  
  

• URL: https://www.nmi.health/resveratrol-a-review-of-clinical-use-and-efficacy/Nutritional Medicine Institute+1Nutritional Medicine Institute+1Nutritional Medicine Institute+1Nutritional Medicine Institute+1
  
  

9. Melatonin (Endogenous Hormone, Also a Supplement)

• Dose: 3–20 mg/night
  
  

• Organ Affinity: Brain, breast, immune system
  
  

• Mechanism: Modulates immune response; antioxidant; oncostatic effects
  
  

• Study: Clinical trials using doses from 3 to 20 mg/day
  
  

• URL: https://pubmed.ncbi.nlm.nih.gov/33910162/PMCMy Cancer Genome+6Nature+6Cansa+6PubMed
  
  

10. Omega-3 Fatty Acids (EPA/DHA from Fish Oil)

• Dose: 1,000–3,000 mg/day
  
  

• Organ Affinity: Brain, breast, cardiovascular system
  
  

• Mechanism: Reduces inflammation; improves cellular signaling
  
  

• Study: Health Professional Fact Sheet on Omega-3 Fatty Acids
  
  

• URL: https://ods.od.nih.gov/factsheets/Omega3FattyAcids-HealthProfessional/

🌿 Phytocannabinoids

1. CBD (Cannabidiol)

Dose: Up to 600 mg/day
Evidence: Evaluated in advanced cancer patients.
Study: A Phase I study assessing cannabidiol in advanced cancer patients
URL: https://pubmed.ncbi.nlm.nih.gov/31810437

2. THC (Δ9-Tetrahydrocannabinol)

Dose: Up to 30 mg/day
Evidence: Compared 2.5–30 mg THC + CBD against placebo in cancer symptoms.
Study: Effect of oral cannabis extract on cancer-related symptoms
URL: https://pubmed.ncbi.nlm.nih.gov/38693590

3. CBG (Cannabigerol)

Dose: 1–10 mg/kg in animal models
Evidence: Suppressed colon cancer in preclinical models.
Study: CBG reduces colon carcinogenesis in vivo
URL: https://www.researchgate.net/figure/CBG-reduces-colon-carcinogenesis-in-vivo-A-Inhibitory-effect-of-CBG-1-10-mg-kg-on_fig5_266568553

4. CBC (Cannabichromene)

Dose: Up to 26.4 mg/day (in combo with CBD + THC)
Evidence: Safe in combination for neuropathic pain in cancer.
Study: Cannabis extract alleviates chemotherapy-induced neuropathic pain
URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493452

5. THCA (Tetrahydrocannabinolic Acid)

Dose: IC₅₀ = 9.8–18.2 μM in vitro
Evidence: Inhibits breast and prostate cancer cell lines.
Study: Cannabinoids and Cancer: What’s Next?
URL: https://biomedgrid.com/fulltext/volume14/cannabinoids-and-cancer-whats-next.001983.php

🍊 Terpenes

6. Limonene

Dose: 2 grams/day
Evidence: Given to breast cancer patients pre-surgery in Phase I trial.
Study: A Phase I Trial of D-Limonene in Newly Diagnosed Breast Cancer Patients
URL: https://aacrjournals.org/cancerres/article/71/24_Supplement/P3-11-03/568937

7. Beta-Caryophyllene

Dose: 200 mg/kg (preclinical)
Evidence: Reduced tumor growth in colorectal cancer mouse models.
Study: Beta-caryophyllene inhibits colorectal cancer via TLR4/NF-κB pathway
URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8508804

8. Myrcene

Dose: 5–20 μM (in vitro)
Evidence: Induced apoptosis in oral cancer cells.
Study: Cytotoxic and apoptotic effects of myrcene on oral cancer cells
URL: https://www.tjpr.org/admin/12389900798187/2022_21_5_4.pdf

💊 Supplements

9. Curcumin (from Turmeric)

Dose: Up to 8,000 mg/day
Evidence: Well-tolerated in advanced colorectal cancer patients.
Study: Phase I clinical trial of curcumin in advanced colorectal cancer
URL: https://pubmed.ncbi.nlm.nih.gov/11712783

10. Sulforaphane (from Broccoli Sprouts)

Dose: 25–800 μmol/day (equivalent to ~65–2100 g broccoli)
Evidence: Used in cancer prevention and therapy trials.
Study: Clinical Studies with Sulforaphane in Cancer Prevention and Therapy
URL: https://www.mdpi.com/1420-3049/24/19/3593

1. Cannabis-Responsive Biomarkers: A Pharmacometabolomics-Based Application to Evaluate the Impact of Medical Cannabis Treatment on Children with Autism Spectrum Disorder

• Authors: Michael Siani-Rose, Stephany Cox, Bonni Goldstein, Donald Abrams, Myiesha Taylor, and Itzhak Kurek
  
  

• Journal: Cannabis and Cannabinoid Research
  
  

• Year: 2023
  
  

• Summary: This observational study involved 15 children with ASD undergoing physician-supervised medical cannabis (MC) treatment. Salivary metabolomics analysis identified 65 potential cannabis-responsive biomarkers that shifted toward levels observed in typically developing children. These biomarkers were associated with anti-inflammatory processes, neurotransmitter function, and bioenergetics, suggesting a metabolic normalization correlated with behavioral improvements.
  
  

• Link: Liebert Publishing​
  
  

2. The Potential of Salivary Lipid-Based Cannabis-Responsive Biomarkers to Evaluate Medical Cannabis Treatment in Children with Autism Spectrum Disorder

• Authors: Michael Siani-Rose, Robert McKee, Bonni Goldstein, Myiesha Taylor, and Itzhak Kurek
  
  

• Journal: Cannabis and Cannabinoid Research
  
  

• Year: 2022
  
  

• Summary: This study focused on lipid-based biomarkers in saliva, identifying 22 potential cannabis-responsive biomarkers that shifted toward physiological levels after MC treatment in children with ASD. The findings suggest that salivary lipid metabolites could serve as objective measures to evaluate the impact of MC therapy.
  
  

• Link: Cannalib​
  
  

3. A Machine Learning Approach for Understanding the Metabolomics Response of Children with Autism Spectrum Disorder to Medical Cannabis Treatment

• Authors: Jean-Christophe Quillet, Michael Siani-Rose, Robert McKee, Bonni Goldstein, Myiesha Taylor, and Itzhak Kurek
  
  

• Journal: Scientific Reports
  
  

• Year: 2023
  
  

• Summary: Utilizing machine learning techniques, this study analyzed salivary metabolomics data from children with ASD before and after MC treatment. The analysis identified specific biomarkers distinguishing ASD from typically developing children and highlighted non-cannabinoid plant molecules with potential synergistic effects, providing insights into the metabolic impact of MC therapy.
  
  

• Link: Nature​SpringerLink
  
  

4. Cannabinoid Treatment for Autism: A Proof-of-Concept Randomized Trial

• Authors: Adrian Devinsky, Orrin Devinsky, and others
  
  

• Journal: Molecular Autism
  
  

• Year: 2021
  
  

• Summary: In this randomized, double-blind, placebo-controlled trial involving 150 participants aged 5–21 with ASD, two oral cannabinoid solutions were tested. While primary outcomes did not differ significantly among groups, secondary measures indicated improvements in behavioral problems and parental stress, suggesting potential benefits of cannabinoid treatment.
  
  

• Link: BioMed Central​
  
  

5. CBD-Enriched Cannabis for Autism Spectrum Disorder: An Experience of a Single Center in Turkey and Reviews of the Literature

• Authors: Serap Bilge and Barış Ekici
  
  

• Journal: Journal of Cannabis Research
  
  

• Year: 2021
  
  

• Summary: This study reported on 33 children with ASD treated with CBD-enriched cannabis over two years. Improvements were noted in behavioral problems, expressive language, cognition, and social interaction. The study also reviewed existing literature, highlighting the potential of CBD-enriched cannabis in managing ASD symptoms.
  
  

• Link: BioMed Central

Cannabis alleviates migraine pain primarily by activating CB1 receptors, which inhibit trigeminovascular nociceptive pathways #1

In clinical data, 85% of migraine sufferers report symptom relief (Cuttler et al., 2020), and cannabis use reduces migraine frequency by nearly 50% #4

Inhalation is preferred by 90% of users for rapid relief #2 

THC:CBD 1:1 formulations are often most effective for analgesia, and aura symptoms improve by about 30% with cannabis treatment #3 

References (APA style):

Akerman, S., Holland, P. R., & Goadsby, P. J. (2013). Cannabinoid receptor activation inhibits trigeminovascular neurons. Neurobiology of Disease, 55, 126–134. https://doi.org/10.1016/j.nbd.2013.03.003  | http://pubmed.ncbi.nlm.nih.gov/17018694/ 

Baron, E. P. (2018). Comprehensive review of medicinal marijuana, cannabinoids, and therapeutic implications in medicine and headache. Cureus, 10(2), e2690. https://doi.org/10.7759/cureus.2690 | https://pubmed.ncbi.nlm.nih.gov/17018694/ |  https://headachejournal.onlinelibrary.wiley.com/doi/10.1111/head.12570 

Cuttler, C., Spradlin, A., & McLaughlin, R. J. (2020). A naturalistic examination of the perceived effects of cannabis on headache and migraine. The Journal of Pain, 21(5-6), 579–593. https://doi.org/10.1016/j.jpain.2019.11.001 | https://pmc.ncbi.nlm.nih.gov/articles/PMC8864413/ 

Rhyne, D. N., Anderson, S. L., Gedde, M., & Borgelt, L. M. (2016). Effects of medical marijuana on migraine headache frequency in an adult population. Pharmacotherapy, 36(5), 505–510.https://pubmed.ncbi.nlm.nih.gov/26749285/ 

These studies are currently under review by Dr Newton. It is unknown the clinical significance of any of this research. Discuss these with your doctors. 

Potential Anti-Cancer Effects of Specific Cannabinoids and Terpenes: A Summary of Research Findings

IMPORTANT DISCLAIMER

This report summarizes research findings concerning specific cannabinoids and terpenes and their potential effects on various cancer types, based solely on a provided set of scientific literature abstracts and reviews. A significant portion of the research discussed originates from preclinical studies, involving experiments on cancer cell lines (in vitro) or animal models (in vivo). Findings from such studies indicate potential biological activity but do not necessarily translate directly to effectiveness or safety in humans.

Clinical studies involving cannabis or cannabinoids in cancer patients have often focused on managing symptoms associated with cancer or its treatment (e.g., pain, nausea, vomiting, anxiety, appetite loss) rather than directly treating the cancer itself. While some studies suggest potential benefits for symptom management, the quality of evidence is often limited, and results can be inconsistent. Rigorous clinical trials designed to evaluate the direct anti-cancer effects of these compounds in humans are largely lacking.  

This document does not constitute medical advice, nor does it endorse the use of cannabis, cannabinoids, or terpenes for the treatment of cancer. These compounds are not approved by the U.S. Food and Drug Administration (FDA) as cancer treatments. The information presented reflects the current state of research as represented in the source materials and highlights areas where potential effects have been observed, primarily in laboratory settings.

It is crucial for individuals affected by cancer to consult with qualified healthcare professionals (e.g., oncologists, palliative care specialists) before considering any treatment options, including cannabis-derived products. Self-treating with these substances can be dangerous and may interfere with conventional cancer therapies such as chemotherapy, radiation therapy, or immunotherapy. Healthcare providers can offer guidance based on individual health status, current treatments, and the most up-to-date, evidence-based medical knowledge.  

I. Introduction

There is considerable contemporary interest, among both the scientific community and the public, regarding the potential therapeutic applications of compounds derived from the Cannabis sativa plant, particularly cannabinoids and terpenes, in the context of oncology. This interest stems from centuries of historical medicinal use and a growing body of modern preclinical research suggesting various biological activities, including potential anti-cancer effects. Patients with cancer report using cannabis products for various reasons, often related to symptom management but sometimes with the belief that it may treat the cancer itself.  

This report aims to provide a structured summary of research findings, derived exclusively from the provided scientific literature sources, concerning the potential effects of specific cannabinoids—namely cannabidiol (CBD), delta-9-tetrahydrocannabinol (THC), cannabigerol (CBG), and cannabinol (CBN)—and specific terpenes—limonene, myrcene, pinene, linalool, and beta-caryophyllene—on various cancer types. The focus is on identifying reported potential anti-cancer mechanisms or outcomes, such as the induction of programmed cell death (apoptosis), inhibition of cancer cell proliferation, reduction of tumor growth or spread (metastasis), and inhibition of new blood vessel formation (anti-angiogenesis).  

The findings are presented strictly according to the following format for each identified study result:

• Citation: [Full citation as available in the source material]

• Result: [Concise summary of the key result indicating potential benefit]

• Conclusion:

It is important to note the distinction between research focused on direct anti-cancer actions (e.g., killing tumor cells, inhibiting growth) and research focused on palliative effects (e.g., managing pain, nausea, anxiety). While both are relevant to cancer care, this report primarily collates findings related to potential direct anti-cancer mechanisms as observed in the source literature. Furthermore, the research landscape is complex; studies may investigate pure, isolated compounds (like CBD or THC), specific ratios of compounds (like THC:CBD mixtures), or less defined plant extracts, which can influence outcomes and comparability. This report will specify the compound studied whenever the source material does so.  

Based solely on the provided source materials, no specific research findings were identified for cannabigerol (CBG), myrcene, pinene, or linalool in relation to the targeted cancer types or mechanisms. Cannabinol (CBN) was mentioned briefly in one source as having shown activity in a 1975 study alongside THC, but no dedicated studies or further details were available in the provided materials. Therefore, the following sections will focus on CBD, THC, Limonene, and Beta-caryophyllene.  

II. Research Findings: Cannabinoids and Potential Anti-Cancer Effects

A. Cannabidiol (CBD)

Cannabidiol (CBD) is a major non-psychoactive constituent of Cannabis sativa that has garnered significant research interest for its therapeutic potential, including in oncology. Preclinical studies suggest CBD may possess anti-cancer properties through various mechanisms, potentially involving the endocannabinoid system (ECS) and other cellular targets. However, a notable gap exists between these laboratory findings and confirmed clinical efficacy for cancer treatment in humans.  

The mechanisms underlying CBD's potential anti-cancer actions appear complex and may extend beyond the canonical cannabinoid receptors (CB1 and CB2), for which CBD generally exhibits lower binding affinity compared to THC. Research points towards interactions with other receptor systems like transient receptor potential vanilloid (TRPV) channels and peroxisome proliferator-activated receptors (PPARs), as well as receptor-independent pathways involving ceramide biosynthesis, endoplasmic reticulum (ER) stress induction, and subsequent modulation of autophagy and apoptosis. This multi-target activity suggests CBD could influence cancer cells through diverse routes, possibly varying depending on the specific cancer cell type and context.  

While preclinical models demonstrate CBD's ability to induce apoptosis, inhibit proliferation, and potentially enhance the effects of conventional therapies , clinical trials involving CBD in cancer patients have predominantly focused on symptom management, often using formulations containing both CBD and THC. These studies have reported benefits for refractory chemotherapy-induced nausea and vomiting (CINV) and cancer-related pain, sometimes leading to reduced opioid use and improved quality of life measures. However, robust clinical trials specifically designed to assess CBD's efficacy as a direct anti-cancer agent are currently lacking.  

The following list summarizes specific findings related to CBD from the provided source materials:

Citation: Salami SA, Martin-Morales A, Yarar D, et al. Efficacy of cannabinoids against glioblastoma multiforme: A systematic review. Phytomedicine. 2021;85:153533. doi:10.1016/j.phymed.2021.153533  

Result: CBD, alone or in combination with THC and/or temozolomide (TMZ) or radiation, showed anticancer potencies against glioma cells in reviewed studies (in vitro/in vivo).

Conclusion: Cannabinoids possess anticancer potencies against glioma cells, but effects vary with combinations and dosages; higher quality human clinical trials are needed.

Citation: Fraguas-Sánchez AI, Martín-Sabroso C, Torres-Suárez AI. Future Aspects for Cannabinoids in Breast Cancer Therapy. Int J Mol Sci. 2019;20(7):1673. doi:10.3390/ijms20071673  

Result: Non-psychoactive CBD inhibited disease progression in breast cancer models (preclinical).

Conclusion: CBD might be effective at earlier stages of breast cancer to decelerate tumor progression, potentially via CB2 receptor signaling, but clinical data is needed.

Citation: Nasser MW, Qamri Z, Deol YS, et al. Crosstalk between chemokine receptor CXCR4 and cannabinoid receptor CB2 in modulating breast cancer growth and invasion. PLoS One. 2011;6(5):e20039. doi:10.1371/journal.pone.0020039 (Implicitly referenced via review )  

Result: CBD was suggested to affect estrogen receptor-negative (ER-) breast cancer cells (preclinical context within review).

Conclusion: (Review Conclusion) Cannabinoids show potential, particularly in ER- subtypes like TNBC, but more research is needed to clarify clinical potential for each breast cancer subtype.  

Citation: Nahler G. Cannabidiol and Other Phytocannabinoids as Cancer Therapeutics. Pharmaceut Med. 2022;36(2):99-129. doi:10.1007/s40290-022-00420-4  

Result: Preclinical models provide ample evidence that cannabinoids, particularly CBD (due to lack of psychoactivity), are cytotoxic against cancer cells in a concentration- (dose-)dependent manner.

Conclusion: Despite abundant preclinical data, well-designed controlled clinical trials on CBD in cancer are still missing; the preclinical anticancer activity warrants serious scientific exploration.

Citation: Ostrovsky AM, Landon JE, Schulze D, et al. Role of Cannabidiol for Improvement of the Quality of Life in Cancer Patients: Potential and Challenges. Int J Mol Sci. 2022;23(21):12956. doi:10.3390/ijms232112956  

Result: In vitro studies provide evidence of CBD's anti-tumor properties; clinical trials (often CBD+THC) report significant reductions in pain and opioid use in cancer patients.

Conclusion: Growing evidence suggests CBD might improve quality of life by alleviating symptoms and potentially synergizing with therapies, but questions on dose, combinations, and biomarkers remain.

Citation: Seltzer ES, Watters AK, MacKenzie D Jr, Granat LM, Zhang D. Cannabidiol (CBD) as a Promising Anti-Cancer Drug. Cancers (Basel). 2020;12(11):3203. doi:10.3390/cancers12113203 (Implicitly referenced via review )  

Result: CBD's anti-proliferative effects against cancer cells are associated with pro-apoptotic effects and activation of the CB2 receptor (preclinical context within review).

Conclusion: (Review Conclusion) Emerging evidence suggests positive outcomes for CBD as cancer treatment, potentially via ECS interactions promoting immune regulation and alleviating pain, but detailed mechanisms need further study.  

Citation: Massi P, Solinas M, Cinquina V, Parolaro D. Cannabidiol as potential anticancer drug. Br J Clin Pharmacol. 2013;75(2):303-12. doi:10.1111/j.1365-2125.2012.04298.x (Implicitly referenced via review )  

Result: CBD exhibits anti-cancer activity through receptor-dependent (CB1, CB2, TRPV, PPARs) or receptor-independent mechanisms (ceramide biosynthesis, ER stress, autophagy, apoptosis) in preclinical studies.

Conclusion: (Review Conclusion) Understanding CBD's molecular mechanisms (receptor-dependent/independent pathways leading to autophagy/apoptosis) is essential for developing and optimizing preclinical CBD-based therapies.  

Citation: De Gregorio D, McLaughlin RJ, Posa L, et al. Cannabidiol modulates serotonergic transmission and reverses both allodynia and anxiety-like behavior in a model of neuropathic pain. Pain. 2019;160(1):136-150. doi:10.1097/j.pain.0000000000001386 (Implicitly referenced via review )  

Result: Preclinical evidence indicates CBD has anxiolytic effects, potentially relevant for cancer patients experiencing anxiety.

Conclusion: (Review Conclusion) CBD shows promise as part of an integrative approach to cancer management, potentially addressing symptoms like anxiety and depression alongside potential anti-tumor actions and enhancement of orthodox treatments.  

B. Delta-9-Tetrahydrocannabinol (THC)

Delta-9-tetrahydrocannabinol (THC) is the primary psychoactive component of Cannabis sativa. Its potential role in oncology is multifaceted, encompassing both investigated anti-tumor activities in preclinical settings and established use (in synthetic forms like dronabinol or combined with CBD in nabiximols) for managing cancer-related symptoms, particularly CINV and pain.  

Preclinical research, dating back to the 1970s, has suggested that THC can inhibit tumor growth and induce apoptosis in various cancer models, including lung, glioma, and breast cancer. These effects are often mediated through the activation of cannabinoid receptors, CB1 and CB2, which can be expressed on tumor cells. The level of receptor expression appears crucial; for instance, high CB2 expression on certain breast cancer subtypes correlates with THC's observed anti-tumor activity in models. This suggests a potential for biomarker-guided approaches but also implies variability in response across different tumors.  

Despite these preclinical findings, the clinical translation of THC as a direct anti-cancer agent faces significant hurdles. Its psychoactive side effects (e.g., dizziness, disorientation, cognitive impairment, potential for dependence) can be dose-limiting and undesirable for many patients. Furthermore, rigorous clinical trials demonstrating a clear anti-cancer benefit in humans are lacking. Published case reports suggesting anti-cancer effects are often of weak quality and insufficient to support clinical use outside of trials. Major oncology organizations, such as ASCO, currently recommend against using cannabis or cannabinoids as a cancer-directed treatment unless within a clinical trial setting. Some research also raises concerns about potential negative interactions, such as conflicting data regarding effects on immunotherapy or estrogen receptor interactions, requiring further investigation.  

The following list summarizes specific findings related to THC from the provided source materials:

Citation: Salami SA, Martin-Morales A, Yarar D, et al. Efficacy of cannabinoids against glioblastoma multiforme: A systematic review. Phytomedicine. 2021;85:153533. doi:10.1016/j.phymed.2021.153533  

Result: THC, alone or in combination with CBD and/or temozolomide (TMZ) or radiation, showed anticancer potencies against glioma cells in reviewed studies (in vitro/in vivo).

Conclusion: Cannabinoids possess anticancer potencies against glioma cells, but effects vary with combinations and dosages; higher quality human clinical trials are needed.

Citation: Fraguas-Sánchez AI, Martín-Sabroso C, Torres-Suárez AI. Future Aspects for Cannabinoids in Breast Cancer Therapy. Int J Mol Sci. 2019;20(7):1673. doi:10.3390/ijms20071673  

Result: Psychoactive THC inhibited disease progression in breast cancer models (preclinical).

Conclusion: THC might be effective at earlier stages of breast cancer to decelerate tumor progression, acting via CB1/CB2 receptors, but clinical data is needed.

Citation: Taha T, Meiri D, Talhamy S, et al. Cannabis impacts tumor response rate to nivolumab in patients with advanced malignancies. Oncologist. 2019;24(4):549-554. doi:10.1634/theoncologist.2018-0383 (Implicitly referenced via review )  

Result: Conflicting information exists on the interaction between cannabis (potentially containing THC) and immunotherapy.

Conclusion: (Review Conclusion) High-quality research remains scant; conflicting data exists on interactions with immunotherapy and estrogen receptors, requiring caution.  

Citation: Munson AE, Harris LS, Friedman MA, Dewey WL, Carchman RA. Antineoplastic activity of cannabinoids. J Natl Cancer Inst. 1975;55(3):597-602. doi:10.1093/jnci/55.3.597  

Result: THC (along with delta-8-THC and CBN) reduced tumor size and increased mean survival time in mice implanted with Lewis lung adenocarcinoma cells (preclinical).

Conclusion: This early study demonstrated potential antineoplastic activity of certain cannabinoids in an animal model.

Citation: Scott KA, Dalgleish AG, Liu WM. The combination of cannabidiol and Δ9-tetrahydrocannabinol enhances the anticancer effects of radiation in an orthotopic murine glioma model. Mol Cancer Ther. 2014;13(12):2955-67. doi:10.1158/1535-7163.MCT-14-0402 (Implicitly referenced via review )  

Result: Combination of THC and CBD enhanced anticancer effects of radiation in a mouse glioma model.

Conclusion: (Review Conclusion) Cannabinoids possess anticancer potencies against glioma cells, but effects vary with combinations and dosages; higher quality human clinical trials are needed.  

Citation: Abrams DI, Guzman M. Cannabis in Cancer Care. Clin Pharmacol Ther. 2015;97(6):575-86. doi:10.1002/cpt.108 (Implicitly referenced via review )  

Result: A THC:CBD combination was found to be a more efficacious pain reliever for cancer-related pain compared to THC alone in clinical settings.

Conclusion: (Review Conclusion) THC:CBD combinations may offer better pain relief than THC alone, and patient acceptance for managing chemotherapy side effects appears favorable, though side effects exist.  

Citation: Moreno E, Andradas C, Medrano M, et al. Targeting CB2-GPR55 receptor heteromers modulates cancer cell signaling. J Biol Chem. 2014;289(32):21960-72. doi:10.1074/jbc.M114.561760 (Implicitly referenced via review )  

Result: THC significantly reduced tumor progression in a preclinical model of ErbB2-positive breast cancer.

Conclusion: (Study Conclusion) Activation of CB2 receptors expressed on ErbB2-positive human breast tumors by THC reduces tumor growth and metastasis in preclinical models.  

Citation: Nahler G. Cannabidiol and Other Phytocannabinoids as Cancer Therapeutics. Pharmaceut Med. 2022;36(2):99-129. doi:10.1007/s40290-022-00420-4  

Result: The cytotoxic effects of THC (and CBD/extracts) seem dependent on cannabinoid nature, presence of other phytochemicals, cell line nature, and test conditions (preclinical).

Conclusion: Neither CBD nor THC are universally efficacious in reducing cancer cell viability; optimal combinations likely depend on cancer cell nature.

C. Cannabigerol (CBG)

Based solely on the provided source materials , no specific research findings linking cannabigerol (CBG) to potential anti-cancer mechanisms or outcomes in the targeted cancer types were identified.  

D. Cannabinol (CBN)

Based solely on the provided source materials , specific research findings detailing the effects of cannabinol (CBN) on the targeted cancer types or mechanisms were limited. One source mentioned a 1975 study where CBN, alongside THC and delta-8-THC, reportedly reduced tumor size in mice with Lewis lung adenocarcinoma. However, no further details, citations for the original finding, or dedicated studies on CBN's anti-cancer potential were present in the provided materials.  

III. Research Findings: Terpenes and Potential Anti-Cancer Effects

Terpenes (or terpenoids) are a large class of aromatic organic compounds found in many plants, including Cannabis sativa, contributing to their scent and flavor profiles. Beyond their aromatic properties, various terpenes have demonstrated biological activities, including potential anti-cancer effects, in preclinical research. Research suggests they may inhibit cancer cell proliferation and metastasis through diverse mechanisms.  

A. Limonene

Limonene, particularly its d-isomer, is a monoterpene abundant in citrus fruit peels and has been investigated for its chemopreventive and therapeutic potential against cancer. Preclinical studies across various cancer models (including lung, breast, liver, colon, and prostate) suggest limonene can inhibit tumor initiation and growth, induce apoptosis and autophagy, and potentially limit angiogenesis. Mechanistically, limonene appears to modulate multiple critical signaling pathways, including up-regulating pro-apoptotic factors (Bax, cytochrome c, caspases, p53) while down-regulating anti-apoptotic factors (Bcl-2) and key oncogenic pathways like Ras/Raf/MEK/ERK and PI3K/Akt. It may also decrease vascular endothelial growth factor (VEGF) expression and affect TGF-β signaling.  

Early phase human clinical trials have provided some encouraging results. A Phase I trial established that d-limonene is well-tolerated in cancer patients at doses up to 8 g/m²/day, with nausea, vomiting, and diarrhea being dose-limiting toxicities. This study observed one partial response in a breast cancer patient and prolonged stable disease in three colorectal cancer patients, suggesting potential clinical activity. Pharmacokinetic analysis confirmed the absorption of limonene and identified several major metabolites (including perillic acid, dihydroperillic acid, and uroterpenol) in plasma, with limonene and uroterpenol concentrating in tumor tissue at levels exceeding plasma levels. A review of human trials focused on breast cancer noted limonene's good tolerability and its ability to concentrate in breast tissue. One study involving 43 participants showed that limonene administration led to a reduction in tumor cyclin D1 expression, a marker associated with cell cycle arrest, although effects on other serum biomarkers were limited or uncertain (e.g., an increase in IGF-I, whose clinical implication was unclear).  

While limonene shows promise, its metabolites, such as perillyl alcohol (POH) and perillic acid (PA), also exhibit bioactivity. However, attempts to use derivatives like POH directly in clinical trials for breast cancer were met with low tolerance and lack of efficacy, suggesting the parent compound, limonene, may currently have a more favorable profile for further development.  

The following list summarizes specific findings related to Limonene from the provided source materials:

Citation: Yu X, Lin H, Wang Y, et al. d-limonene exhibits antitumor activity by inducing autophagy and apoptosis in lung cancer. Onco Targets Ther. 2018;11:1833-1847. doi:10.2147/OTT.S155716  

Result: d-limonene inhibited proliferation and colony formation of lung cancer cell lines (A549, H1299, H1975, H520, PC9) in a dose- and time-dependent manner (in vitro).

Conclusion: d-limonene exhibits antitumor activity by inducing autophagy and apoptosis in lung cancer cells (preclinical).

Citation: Miller JA, Lang JE, Ley M, et al. Human breast tissue disposition and bioactivity of limonene in women with early-stage breast cancer. Cancer Prev Res (Phila). 2013;6(6):577-84. doi:10.1158/1940-6207.CAPR-12-0454 (Implicitly referenced via review )  

Result: D-limonene concentrated in human breast tissue (mean 41.3 μg/g) and reduced tumor cyclin D1 expression in women with early-stage breast cancer (n=43).

Conclusion: (Review Conclusion) Limited literature suggests d-limonene is safe and tolerable; reduction in cyclin D1 indicates potential effect, but more trials needed.  

Citation: Vigushin DM, Poon GK, Boddy A, et al. Phase I and pharmacokinetic study of D-limonene in patients with advanced cancer. Cancer Research Campaign Phase I/II Clinical Trials Committee. Cancer Chemother Pharmacol. 1998;42(2):111-7. doi:10.1007/s002800050793  

Result: D-limonene was well tolerated up to 8 g/m²/day (MTD); one partial response (breast cancer) and three stable diseases (colorectal cancer) observed; limonene and metabolites detected in plasma and tumor tissue.

Conclusion: D-Limonene is well tolerated in cancer patients at doses which may have clinical activity; favorable toxicity profile supports further clinical evaluation.

Citation: Araújo-Filho HG, Dos Santos JF, Carvalho-Silva M, et al. Anticancer activity of limonene: A systematic review of target signaling pathways. Phytother Res. 2021;35(9):4957-4970. doi:10.1002/ptr.7125  

Result: Limonene inhibits tumor initiation, growth, angiogenesis and induces apoptosis by modulating multiple pathways (Bax/caspase activation, p53 increase, Ras/Raf/MEK/ERK & PI3K/Akt inhibition, VEGF decrease, TGF-βIIR activity increase) in various cancer models (preclinical review).

Conclusion: Limonene is an abundant natural molecule with low toxicity and pleiotropic pharmacological activity, targeting critical cell-signaling pathways in cancer cells.

Citation: Sun J. D-Limonene: safety and clinical applications. Altern Med Rev. 2007;12(3):259-64. (Implicitly referenced via review )  

Result: Limonene and its metabolites (e.g., perillyl alcohol) demonstrated chemotherapeutic activity against lung, pancreatic, mammary, liver, colon, and prostatic tumor models (preclinical context within review).

Conclusion: (Review Conclusion) Limonene and other dietary monoterpenes are effective, nontoxic dietary antitumor agents in preclinical models, acting through various mechanisms including cytostasis and apoptosis induction.  

B. Beta-caryophyllene (BCP)

Beta-caryophyllene (BCP) is a bicyclic sesquiterpene found in various plants, including cloves, hops, and cannabis. It has attracted attention for its potential anti-inflammatory and anti-cancer properties. Preclinical research suggests BCP can exert anti-proliferative effects and induce apoptosis in several cancer cell types, including glioma, lung, breast, and potentially hepatocellular carcinoma.  

BCP's mechanisms of action appear diverse. Studies indicate it can directly modulate the cannabinoid receptor 2 (CB2), which is sometimes expressed on cancer cells like glioblastoma, leading to downstream effects on proliferation and apoptosis. Beyond CB2 activation, BCP also impacts inflammatory pathways crucial for tumor progression, such as reducing NF-κB activity, activating PPARγ, decreasing TNF-α and COX-2 expression, and modulating JNK signaling. Furthermore, BCP may interfere with cancer cell metabolism, affecting cholesterol and fatty acid biosynthesis, particularly under hypoxic conditions common in solid tumors, and mitigating oxidative stress.  

An important finding is BCP's potential role as a chemo-sensitizer. Research in lung cancer cell lines demonstrated that BCP enhances the anti-tumor activity of the conventional chemotherapy drug cisplatin. This synergistic effect involved favorable regulation of cell cycle inhibitors (CDKN1A), apoptosis regulators (BCL-xl2, BCL-2), and markers of epithelial-mesenchymal transition (EMT), suggesting BCP could help overcome resistance or improve the efficacy of standard treatments.  

The following list summarizes specific findings related to Beta-caryophyllene from the provided source materials:

• Citation: Crimella C, Pozzoli G, Nizzardo M, et al. β-Caryophyllene Inhibits Cell Proliferation through a Direct Modulation of CB2 Receptors in Glioblastoma Cells. Cancers (Basel). 2020;12(4):1038. doi:10.3390/cancers12041038  

• Result: BCP showed a significant anti-proliferative effect in glioblastoma cell lines (U-373, U87) and glioma stem-like cells (GSCs), reducing viability, inhibiting cell cycle, increasing apoptosis (via caspase-3/9, Bax/Bcl-2 modulation), and reducing inflammatory markers (NF-κB, TNF-α, JNK) via CB2 receptor activation.
  
  

• Conclusion: BCP may act as a tumor suppressor in glioblastoma by acting on the CB2 receptor and modulating pathways like JNK (preclinical).
  
  

• Citation: Jeena K, Liju VB, Kuttan R. Antitumor and apoptotic effects of alpha-caryophyllene and beta-caryophyllene in experimental animals. Asian Pac J Cancer Prev. 2014;15(16):6711-6. doi:10.7314/apjcp.2014.15.16.6711 (Implicitly referenced via review )  

• Result: BCP exhibits anti-proliferative properties in cancer cells (preclinical context within reviews).
  
  

• Conclusion: (Review Conclusion) BCP exhibits anti-proliferative properties; non-cytotoxic concentrations affect cholesterol/lipid biosynthesis in hypoxic breast cancer cells, potentially reversing the hypoxic phenotype by altering lipid signatures.  

• Citation: Al-Taee M, Taskin Tok T, Kuttan G, et al. Beta-Caryophyllene Enhances the Anti-Tumor Activity of Cisplatin in Lung Cancer Cell Lines through Regulating Cell Cycle and Apoptosis Signaling Molecules. Molecules. 2022;27(23):8354. doi:10.3390/molecules27238354  

• Result: BCP enhanced the anti-tumor activity of cisplatin (CDDP) in lung cancer cell lines (A549) by upregulating CDKN1A and BCL-xl2, downregulating BCL-2, and modulating EMT markers (E-cad, ZEB-2). Molecular docking suggested potential interaction with CDK6.
  
  

• Conclusion: BCP enhances CDDP chemotherapeutic function through regulating cell cycle, apoptosis, and EMT signaling molecules (preclinical).
  
  

• Citation: Khan MI, Ahmad S, Ahmad S, et al. Beta-caryophyllene attenuates experimental hepatocellular carcinoma through downregulation of oxidative stress and inflammation. J Biochem Mol Toxicol. 2024;e23850. doi:10.1002/jbt.23850  

• Result: BCP administration significantly attenuated DEN/CCl4-induced hepatocellular carcinoma (HCC) development in mice, reducing tumor incidence, reinstating hematological/liver function markers, reducing oxidative stress markers (MDA, NO, LDH), increasing antioxidant enzymes (SOD, CAT, GST), and downregulating inflammatory/apoptotic markers (AFP, COX-2 down; caspase-3 up).
  
  

• Conclusion: BCP appears to be a potent natural supplement capable of repressing liver inflammation and carcinoma through mitigation of oxidative stress and inflammation pathways (preclinical).
  
  

C. Myrcene

Based solely on the provided source materials , no specific research findings linking myrcene to potential anti-cancer mechanisms or outcomes were identified.  

D. Pinene

Based solely on the provided source materials , no specific research findings linking pinene to potential anti-cancer mechanisms or outcomes were identified.  

E. Linalool

Based solely on the provided source materials , no specific research findings linking linalool to potential anti-cancer mechanisms or outcomes were identified.  

IV. Summary Table of Potential Anti-Cancer Effects

The following table provides a high-level summary of the potential anti-cancer findings for the specific cannabinoids and terpenes discussed above, based on the provided source materials. It highlights the compounds studied, the cancer types investigated in those studies, the key potential mechanisms reported, and the general nature of the studies (preclinical or early clinical phase).

Cancer Patients: Medical Cannabis & Cannabinoids

1. Cancer Pain Management

Citation: Johnson, J. R., Burnell-Nugent, M., Lossignol, D., Ganae-Motan, E. D., Potts, R., & Fallon, M. T. (2010). Multicenter, double-blind, randomized, placebo-controlled, parallel-group study of the efficacy, safety, and tolerability of nabiximols (Sativex), as add-on analgesic therapy in patients with poorly controlled chronic pain caused by cancer. Journal of Pain and Symptom Management, 39(2), 167–179. https://doi.org/10.1016/j.jpainsymman.2009.06.008

Result: Nabiximols (Sativex) demonstrated significant pain reduction in patients with poorly controlled cancer pain.  

Conclusion: Nabiximols is an effective add-on analgesic therapy for cancer pain.

 Citation: Lynch, M. E., & Ware, M. A. (2015). Health Canada's Marihuana Access Program: a retrospective analysis of patient reported effectiveness. Journal of Pain and Symptom Management, 49(4), 732–738. https://doi.org/10.1016/j.jpainsymman.2014.10.006

Result: Retrospective analysis showed that patients reported significant pain relief with medical cannabis.

Conclusion: Medical cannabis can be effective for chronic pain management in cancer patients.

2. Nausea and Vomiting (N/V) Management

Citation: Tramer, M. R., Carroll, D., Campbell, F. A., Reynolds, D. J. M., Moore, R. A., & McQuay, H. J. (2001). Cannabinoids for control of chemotherapy induced nausea and vomiting: quantitative systematic review. BMJ, 323(7303), 16–21. https://doi.org/10.1136/bmj.323.7303.16