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

Javier Rosas

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Table of Contents

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

What Happens When You Talk to AI
Why AI Sounds Confident But Gets It Wrong
Where the Training Data Came From (And Why That Matters)
The Six-Platform Study — What I Found When I Tested Clinical AI
When AI Assumes Resources You Don’t Have
The Failure Modes That Matter Most
A Clinician’s Framework for Evaluating AI Tools
Prompting Like a Clinician
The Regulatory Landscape — What Clinicians Need to Know
The Future of Clinical AI — From the Edge of Medicine
Appendix A: Glossary of AI Terms for Clinicians
Appendix B: AI Evaluation Checklist for Clinicians
- Regulatory Status Quick Check
- Documentation Template
Appendix C: Prompting Templates for Common Clinical Scenarios

AI Literacy for Clinicians: What Every Physician Needs to Know Before Trusting the Algorithm/Why AI Sounds Confident But Gets It Wrong

Why AI Sounds Confident But Gets It Wrong

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The Terminology Problem

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The Anatomy of a Confident Error

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Why “Just Verify It” Is Harder Than It Sounds

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The Five Failure Modes That Matter Most

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1. Formulary-Blind Recommendations

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2. Citation Fabrication

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3. Contraindication Blindness

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4. The “Too Clean” Answer

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5. The Stale Guideline Problem

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What the Research Shows

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The Ship at 2:47 AM, Continued

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Toward a Different Kind of Verification

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Before You Turn the Page

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What Happens When You Talk to AI

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

In this chapter

Why AI Sounds Confident But Gets It Wrong
The Terminology Problem
The Anatomy of a Confident Error
Why “Just Verify It” Is Harder Than It Sounds
The Five Failure Modes That Matter Most
What the Research Shows
The Ship at 2:47 AM, Continued
Toward a Different Kind of Verification
Before You Turn the Page