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1
The AI health information landscape
Consumer vs clinical AI governance gaps student survey
2
Measuring AI health quality — the PAIR study and beyond
PAIR methodology Ayers JAMA research critique
3
The confidence gap — trust, perception, and epistemic risk
Epistemic framing Dunning-Kruger parallel position paper
4
Algorithmic bias in healthcare — from data to disparity
Obermeyer Science study bias detection bridge to DTB
5
Race, medicine, and the machine — historical roots of AI bias
Dorothy Roberts Cartwright eGFR race correction
6
Evaluating AI health information — advanced VERIFY practice
Protocol design systematic evaluation peer review
7
Communicating AI risk — from research to public understanding
Science communication media literacy public translation
8
Capstone — AI health literacy in practice
Research paper intervention design policy brief media project
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