Resources
Tools, frameworks, reading lists, and reference materials for working with AI in clinical practice.
Key Frameworks
These frameworks are referenced throughout the course and serve as practical tools for evaluating and governing clinical AI.
AI Tools for Clinicians
These tools are referenced in the course and may be useful for clinical AI exploration and practice.
| Tool | Use Case | Key Considerations |
|---|---|---|
| NotebookLM | Document-grounded AI assistant for summarizing guidelines, papers | Limited to uploaded sources; useful for controlled exploration |
| Claude | General-purpose AI assistant with strong reasoning | Check institutional policies; avoid PHI in consumer version |
| GPT-4 | General-purpose AI with medical knowledge | May hallucinate; verify all clinical information |
| Perplexity | AI search with citations | Good for research; citations help verification |
| Elicit | Research assistant for literature review | Helpful for rapid evidence synthesis |
Never enter protected health information (PHI) into consumer AI tools unless your institution has a BAA in place. This includes patient names, dates, medical record numbers, and any identifiable clinical details.
Professional Guidelines
Complete Reading List
All required and optional readings from the course, organized by topic.
Foundations
- Topol, E. "High-performance medicine: the convergence of human and artificial intelligence" Nature Medicine, 2019
- Rajpurkar, P. et al. "AI in health and medicine" Nature Medicine, 2022
- Obermeyer, Z. et al. "Dissecting racial bias in an algorithm used to manage the health of populations" Science, 2019
- Wong, A. et al. "External Validation of a Widely Implemented Proprietary Sepsis Prediction Model" JAMA Internal Medicine, 2021
- Mongan, J. et al. "Checklist for Artificial Intelligence in Medical Imaging (CLAIM)" Radiology: AI, 2020
- Correa Soto, R.A. et al. "Parallel pressures: the common roots of doctor bullshit and large language model hallucinations" BMJ, 2025
Generative AI
- Singhal, K. et al. "Large language models encode clinical knowledge" Nature, 2023
- Nori, H. et al. "Capabilities of GPT-4 on Medical Challenge Problems" Microsoft Research, 2023
- Lee, P. et al. "Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine" NEJM, 2023
- Wei, J. et al. "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" NeurIPS, 2022
Governance
- Price, W.N. et al. "Potential Liability for Physicians Using Artificial Intelligence" JAMA, 2019
- Liu, X. et al. "CONSORT-AI extension" Lancet Digital Health, 2020
- Cohen, I.G. "Informed Consent and Medical Artificial Intelligence" Georgetown Law Journal, 2020
- Reddy, S. et al. "A governance model for the application of AI in health care" JAMIA, 2020
Ongoing Learning
Stay current with these resources:
- Nature Medicine — AI and digital health section
- NEJM AI — New journal dedicated to AI in medicine
- The Medical Futurist — Accessible analysis of healthcare technology
- STAT News — Healthcare industry and policy coverage
- AI in Healthcare Newsletter (Ronald Gosselin) — Weekly digest
- This Week in Health IT — Podcast covering health tech developments
Glossary
AI Triad
The three fundamental components of any AI system: data (what it learns from), algorithms (how it learns), and compute (the processing power that enables learning).
Automation Bias
The tendency to over-rely on automated systems and accept their outputs uncritically, even when contradicted by other information.
Centaur
A human-AI collaboration model with clear division of labor—human handles strategy and judgment, AI handles calculation and data processing.
Chain-of-Thought Prompting
A prompting technique that asks AI to show its reasoning step-by-step before providing a final answer, making its logic visible for evaluation.
Cyborg
A human-AI collaboration model with seamless integration, where the boundary between human and machine cognition is blurred.
External Validation
Testing an AI model on data from a different institution or population than where it was developed.
Few-Shot Prompting
Providing examples of desired input-output pairs before asking the AI to perform a task.
Hallucination
When an AI generates plausible but factually incorrect information.
Label Bias
Bias introduced through the outcome variable being predicted—when the "ground truth" itself encodes discrimination.
Proxy Variable
A measurable variable used as a stand-in for something difficult to measure directly—can encode hidden biases.
Shadow AI
Use of AI tools outside institutional oversight and governance, often consumer products used for clinical work.
Temporal Validation
Testing an AI model on data from a different time period than the training data.
Zero-Shot Prompting
Asking an AI to perform a task without providing examples—relying on its pre-training.