This study introduces a dataset and benchmarking pipeline using synthetic personas to evaluate and optimize large language models (LLMs) for tropical and infectious diseases (TRINDs). While LLMs have demonstrated potential in medical question-answering across various formats and sources, their application in health and medical fields remains an active area of exploration. Contributions like Med-Gemini, MedPaLM, AMIE, and Multimodal Medical AI highlight the progress in leveraging LLMs for healthcare, particularly in low-resource settings. Here, LLMs can act as decision-support tools, improving clinical diagnostics, accessibility, multilingual support, and community-level health training. However, despite successes in existing benchmarks, uncertainties persist regarding LLM performance in specialized areas such as TRINDs. The new dataset addresses this gap by simulating diverse user personas to better understand model capabilities and limitations in real-world scenarios, ultimately aiming to enhance LLM utility for global health challenges.

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