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

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

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The Corpus: What the Model Read

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What RLHF Actually Does (And What It Cannot)

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Bias: What the Data Does Not Contain

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The Forty-Drug Formulary Problem

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The Knowledge Cutoff and the Guideline Lag

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Why the Model Treats UpToDate and Reddit the Same

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Before You Turn the Page

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Why AI Sounds Confident But Gets It Wrong

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

In this chapter

Where the Training Data Came From (And Why That Matters)
The Corpus: What the Model Read
What RLHF Actually Does (And What It Cannot)
Bias: What the Data Does Not Contain
The Forty-Drug Formulary Problem
The Knowledge Cutoff and the Guideline Lag
Why the Model Treats UpToDate and Reddit the Same
Before You Turn the Page