ArogyaSutra: A Multi-Agent Framework for Multimodal Medical Reasoning in Indic Languages

2026-06-11 · Source: Takara TLDR - Daily AI Papers · Field: Health & Wellbeing — Medical Devices & Health Technology, Clinical Care & Medical Practice, Health & Medical Research · Depth: Expert, medium

Summary

ArogyaSutra is a multi-agent framework enhancing multimodal medical reasoning in Indic languages. It addresses limitations of English-centric MLLMs in low-resource healthcare, like rural India. The framework uses ArogyaBodha, a comprehensive multilingual multimodal medical question-answer dataset. This dataset compiles eight heterogeneous sources, covering 31 body systems, six imaging modalities, and 21 clinical domains. It spans English and seven major Indian languages. ArogyaSutra employs an actor-critic-based multi-agent architecture. It integrates tool grounding with dual-memory mechanisms for step-wise, reasoning-aware decision making. The framework also uses stored actor-critic simulation trajectories for distillation. Experiments confirm that ArogyaBodha and ArogyaSutra improve multilingual medical reasoning accuracy across all Indic languages. Ablation studies validate each component's contribution.

Key takeaway

For Machine Learning Engineers developing healthcare AI for diverse linguistic regions, ArogyaSutra demonstrates a critical path forward. You should prioritize creating specialized multilingual multimodal datasets like ArogyaBodha. This overcomes limitations of English-centric models. Consider implementing actor-critic multi-agent frameworks with tool grounding and dual-memory. This improves reasoning accuracy and reliability in complex medical scenarios, especially for Indic languages. This approach can significantly enhance equitable access to AI-driven healthcare assistance.

Key insights

Multimodal medical reasoning in low-resource Indic languages benefits from specialized multi-agent frameworks and comprehensive multilingual datasets.

Principles

• Multilingual MLLMs need specialized datasets.
• Actor-critic multi-agent systems enhance reasoning.
• Tool grounding and dual-memory improve decision making.

Method

ArogyaSutra uses an actor-critic multi-agent system with tool grounding and dual-memory for step-wise reasoning, distilling knowledge from simulation trajectories.

In practice

• Develop multilingual datasets for specialized domains.
• Implement actor-critic agents for complex reasoning tasks.
• Utilize tool grounding in multimodal AI systems.

Topics

• Multimodal Medical Reasoning
• Multi-Agent Systems
• Indic Languages
• Healthcare AI
• Tool Grounding
• ArogyaBodha Dataset

Code references

• NUS-Project/MedMASLab

Best for: NLP Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.