Towards Evaluating the Diagnostic Ability of LLMs

Peter Sarvari; Zaid Al-fagih

doi:10.20944/preprints202409.0688.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 6 September 2024 / Approved: 9 September 2024 / Online: 9 September 2024 (12:46:20 CEST)

On average, one in ten patients die because of a diagnostic error and medical errors are the third largest cause of death in the word. While LLMs have been proposed to help doctors with diagnoses, no research results have been published on comparing the diagnostic ability of many popular LLMs on an openly accessible real-patient cohort. In thus study, we compare LLMs from Google, OpenAI, Meta, Mistral, Cohere and Anthropic using our previously published evaluation methodology and explore improving their accuracy with RAG.

Generative AI; LLM; GPT-4; RAG; clinical medicine; diagnosis

Public Health and Healthcare, Primary Health Care

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Towards Evaluating the Diagnostic Ability of LLMs

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