A 'Silent Trial' Assessing the Accuracy of Large Language Models for Assisting Community Health Workers in Low-Resource Settings

2026-02-17 primary care research Title + abstract only

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Community health workers (CHWs) in low-resource settings deliver variable-quality care. This study used OpenAIs o3 and Googles Gemini Flash 2.5 to evaluate whether large language models (LLMs) listening to CHW-patient interactions could generate accurate referral decisions. Across 150 participating Rwandan CHWs, 429 encounters were recorded (in Kinyarwanda) and then processed by LLMs. CHWs demonstrated high referral accuracy (97.9% [95% CI: 96.1%-98.9%]), and OpenAIs o3 performed similarly to C...

A 'Silent Trial' Assessing the Accuracy of Large Language Models for Assisting Community Health Workers in Low-Resource Settings

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