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AI Literacy for Clinicians: What Every Physician Needs to Know Before Trusting the Algorithm

What Every Physician Needs to Know Before Trusting the Algorithm

Javier Rosas

A ship physician who practices 1,000 miles from shore and trains medical AI systems explains what every clinician needs to know about the technology that is quietly reshaping their practice.

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About

About

About the Book

It is 2:47 AM. You are the only physician on a cruise ship in the South Pacific. A patient presents with acute chest pain and a blood pressure of 88/54. You open a clinical AI tool and ask for help. Within seconds, it generates a confident, detailed response.

The AI recommended a drug that was not on board.

This book is written for clinicians who are already using AI in their practice, or who soon will be, and who need to understand what these systems actually do before relying on them for patient care. Not the marketing version. Not the computer science version. The clinical version.

Dr. Javier Rosas practices medicine 1,000 miles from the nearest hospital as a ship physician for Royal Caribbean International. He also trains and evaluates medical AI systems across multiple platforms. That dual perspective, practicing at the extreme edge of medicine while building the tools meant to support it, shapes every chapter.

AI Literacy for Clinicians breaks down how large language models work, why they fail, and what practicing physicians need to know to use them safely. No jargon. No hype. Just the clinical clarity you would expect from a colleague who has tested these tools where the margin for error is zero.

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Author

About the Author

Javier Rosas

Dr. Javier Rosas is a ship physician for Royal Caribbean International with over ten years of clinical experience in emergency and maritime medicine. He manages healthcare for populations of 3,000 to 6,000 passengers and crew, often practicing more than 1,000 miles from the nearest hospital with a formulary of roughly 40 medications, no advanced imaging, and no specialist backup.

He is also a clinical AI specialist who trains and evaluates medical large language models across multiple platforms, including work in RLHF, prompt engineering, and clinical safety evaluation. His research comparing clinical AI tools against general-purpose LLMs using FDA prescribing rubrics revealed a 2.4x performance gap and significant patient safety concerns in frontier models.

This dual practice, delivering care at the extreme edge of medicine while building the AI systems intended to support clinicians, gives him a perspective that no office-based AI researcher or hospital-based physician can replicate. He writes about healthcare AI at theremoteaidoc.com.

Contents

Table of Contents

What Happens When You Talk to AI

  1. The Most Important Thing to Understand
  2. Why This Matters Clinically
  3. How the Model Learned What It Knows
  4. The Training Data Problem
  5. How AI Reads Your Question
  6. Context Windows: What the AI Can See
  7. How the Response Is Built
  8. What This Means for Your Practice
  9. Before You Turn the Page

Why AI Sounds Confident But Gets It Wrong

  1. The Terminology Problem
  2. The Anatomy of a Confident Error
  3. Why “Just Verify It” Is Harder Than It Sounds
  4. The Five Failure Modes That Matter Most
  5. What the Research Shows
  6. The Ship at 2:47 AM, Continued
  7. Toward a Different Kind of Verification
  8. Before You Turn the Page

Where the Training Data Came From (And Why That Matters)

  1. The Corpus: What the Model Read
  2. What RLHF Actually Does (And What It Cannot)
  3. Bias: What the Data Does Not Contain
  4. The Forty-Drug Formulary Problem
  5. The Knowledge Cutoff and the Guideline Lag
  6. Why the Model Treats UpToDate and Reddit the Same
  7. Before You Turn the Page

The Six-Platform Study — What I Found When I Tested Clinical AI

  1. Why I Built This Study
  2. The Six Platforms
  3. The Rubric: 20 Items, 100 Points, One Standard
  4. The Results
  5. What the Clinical AI Tools Got Right
  6. Where the General LLMs Failed
  7. What the 2.4x Gap Actually Means
  8. The Benchmark Paradox
  9. What No Rubric Captures
  10. Why the Gap Exists: Architecture, Not Intelligence
  11. What This Means for Your Practice
  12. Study Limitations
  13. Before You Turn the Page

When AI Assumes Resources You Don’t Have

  1. The Default Environment
  2. The Maritime Stress Test
  3. Five Scenarios the Algorithm Gets Wrong
  4. The Evacuation Decision: What Algorithms Cannot Weigh
  5. Beyond the Ship: The Global Resource Gap
  6. Building AI That Knows Your Constraints
  7. What This Means for You
  8. Before You Turn the Page

The Failure Modes That Matter Most

  1. Drug Interaction Blindness
  2. Context Window Degradation
  3. Anchoring Bias Amplification
  4. Copy-Paste Liability: When AI Output Enters the Record
  5. Shadow AI: The Tools Nobody Talks About
  6. The Compound Effect
  7. Before You Turn the Page

A Clinician’s Framework for Evaluating AI Tools

  1. The Five Questions
  2. Red Flags in AI Product Marketing
  3. When the Institution Chooses for You
  4. Before You Turn the Page

Prompting Like a Clinician

  1. The Specificity Principle
  2. Structured Prompting Templates
  3. The Five Most Common Prompting Mistakes
  4. Eliciting Uncertainty
  5. Ship-Specific Prompting: Lessons from the Edge
  6. Documenting AI-Assisted Decisions
  7. Before You Turn the Page

The Regulatory Landscape — What Clinicians Need to Know

  1. What the FDA Has Done So Far
  2. The CDS Exemption — The Most Important Regulatory Concept You May Not Know
  3. The Gap Between Clearance and Safety
  4. Who Is Liable When AI Is Wrong?
  5. What Is Happening in Europe
  6. The American Patchwork
  7. What Clinicians Should Actually Do
  8. Before You Turn the Page

The Future of Clinical AI — From the Edge of Medicine

  1. The 80/20 Problem
  2. Architecture Is the Safety Layer
  3. The Reproducibility Problem
  4. What I Learned From Breaking Things
  5. The Physician’s Role in Building Better AI
  6. From the Edge of Medicine
  7. A Personal Note

Appendix A: Glossary of AI Terms for Clinicians

  1. How the Models Work
  2. Training and Alignment
  3. Errors and Failure Modes
  4. Architecture and Safety
  5. Regulation and Governance
  6. Evaluation and Benchmarks

Appendix B: AI Evaluation Checklist for Clinicians

  1. Regulatory Status Quick Check
  2. Documentation Template

Appendix C: Prompting Templates for Common Clinical Scenarios

  1. Template 1: Acute Pharmacological Management
  2. Template 2: Differential Diagnosis with Resource Constraints
  3. Template 3: Drug Interaction and Safety Review
  4. Template 4: Evacuation and Transfer Decision Support
  5. Template 5: Evidence Synthesis and Guideline Review
  6. Template 6: Adversarial Follow-Up (Universal)
  7. A Final Note on These Templates

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