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/The Failure Modes That Matter Most

The Failure Modes That Matter Most

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Drug Interaction Blindness

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Context Window Degradation

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Anchoring Bias Amplification

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Copy-Paste Liability: When AI Output Enters the Record

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Shadow AI: The Tools Nobody Talks About

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The Compound Effect

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Before You Turn the Page

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When AI Assumes Resources You Don’t Have

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A Clinician’s Framework for Evaluating AI Tools

In this chapter

The Failure Modes That Matter Most
Drug Interaction Blindness
Context Window Degradation
Anchoring Bias Amplification
Copy-Paste Liability: When AI Output Enters the Record
Shadow AI: The Tools Nobody Talks About
The Compound Effect
Before You Turn the Page