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Large language models for frontline healthcare support in low-resource settings
3
Zitationen
13
Autoren
2026
Jahr
Abstract
< 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.
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