ArogyaSutra: A Multi-Agent Framework for Multimodal Medical Reasoning in Indic Languages
Tanmoy Kanti Halder, Akash Ghosh, Subhadip Baidya, Arijit Roy, Sriparna Saha
ArogyaSutra combines a new dataset (ArogyaBodha) with a multi-agent reasoning framework targeting multilingual medical AI in Indian languages. ArogyaBodha aggregates data from eight sources across 31 body systems, six imaging modalities, and 21 clinical domains, covering English plus seven Indic languages. The ArogyaSutra framework layers an actor-critic multi-agent loop with dual-memory mechanisms and distillation from stored simulation trajectories, claiming accuracy gains across all seven Indic languages on multilingual medical VQA benchmarks β though no absolute numbers are visible in the abstract.
No production traction yet. Zero citations and the GitHub references are all automated daily-paper aggregator bots, not implementations or forks. The project page exists (iitp-cse.github.io/ArogyaSutra) and code and dataset are promised, but there's no downstream adoption signal. Worth watching if you're building healthcare AI for South Asian markets, but this is purely at the research-release stage.
Multimodal Large Language Models (MLLMs) have shown promising reasoning capabilities in general domains, yet their performance remains limited in specialized settings such as healthcare, especially in multilingual and low-resource scenarios. This gap is critical in regions like rural India, where patients often express complex medical queries in native Indic languages and rely on multimodal inputs such as medical images. Existing English-centric MLLMs struggle to support such use cases, limiting equitable access to AI-driven healthcare assistance. To address this challenge, we introduce ArogyaBodha, a large-scale multilingual multimodal medical question-answer dataset constructed from eight heterogeneous sources, covering 31 body systems, six imaging modalities, and 21 clinical domains across English and seven major Indian languages. We further propose ArogyaSutra, an actor-critic-based multi-agent framework that integrates tool grounding with dual-memory mechanisms for step-wise, reasoning-aware decision making, and uses stored actor-critic simulation trajectories for distillation. Experiments show that our dataset and framework improve multilingual medical reasoning accuracy across all Indic languages, with ablations validating the contribution of each component. The source code and dataset are available at: https://iitp-cse.github.io/ ArogyaSutra/