Large language models for frontline healthcare support in low-resource settings

Abstract Large language models (LLMs) have demonstrated strong performance in medical contexts; however, existing benchmarks often fail to reflect the real-world complexity of low-resource health systems accurately. Here we develop a dataset of 5,609 clinical questions contributed by 101 community health workers across 4 Rwandan districts and compared responses generated by 5 LLMs (Gemini-2, GPT-4o, o3-mini, Deepseek R1 and Meditron-70B) with those from local clinicians. A subset of 524 question–answer pairs was evaluated using a rubric of 11 expert-rated metrics, scored on a 5-point Likert scale. Gemini-2 and GPT-4o were the best performers (achieving mean scores of 4.49 and 4.48 out of 5, respectively, across all 11 metrics). All LLMs significantly outperformed local clinicians ( P  < 0.001) across all metrics, with Gemini-2, for example, surpassing local general practitioners by an average of 0.83 points on every metric (range 0.38–1.10). Although performance degraded slightly when LLMs communicated in Kinyarwanda, the LLMs remained superior to clinicians and were over 500 times cheaper per response. These findings support the potential of LLMs to strengthen frontline care quality in low-resource, multilingual health systems.