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/A Clinician’s Framework for Evaluating AI Tools

A Clinician’s Framework for Evaluating AI Tools

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The Five Questions

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Question 1: What is this tool actually doing?

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Question 2: What was it validated against?

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Question 3: Does it know what it does not know?

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Question 4: What happens when it is wrong?

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Question 5: Does the evidence justify the integration?

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Red Flags in AI Product Marketing

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When the Institution Chooses for You

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Before You Turn the Page

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The Failure Modes That Matter Most

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Prompting Like a Clinician

In this chapter

A Clinician’s Framework for Evaluating AI Tools
The Five Questions
Red Flags in AI Product Marketing
When the Institution Chooses for You
Before You Turn the Page