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

Researchers have introduced ArogyaSutra, a specialized multi-agent framework designed to bridge the gap in multimodal medical reasoning for low-resource Indic languages, addressing the limitations of English-centric Multimodal Large Language Models (MLLMs) in rural healthcare settings.

Addressing the Gap in Multilingual Medical AI

While Multimodal Large Language Models (MLLMs) have demonstrated significant reasoning capabilities across general domains, their efficacy diminishes when applied to specialized fields like healthcare. This performance degradation is particularly acute in multilingual and low-resource scenarios, where the lack of high-quality training data in native languages hinders the deployment of reliable AI diagnostic assistants.

The Challenge of Indic Language Integration

In regions such as rural India, healthcare accessibility is often hampered by language barriers. Patients frequently communicate complex medical queries in native Indic languages and provide multimodal inputs—such as medical imaging—to describe their conditions. Current state-of-the-art MLLMs, which are predominantly optimized for English, struggle to accurately interpret these multimodal inputs when paired with Indic linguistic nuances, creating a critical gap in equitable healthcare AI.

The ArogyaSutra Framework

ArogyaSutra is proposed as a multi-agent framework specifically engineered for multimodal medical reasoning. By leveraging a multi-agent architecture, the system aims to improve the processing of complex medical queries by coordinating specialized agents to handle the intersection of medical image analysis and native language understanding. This approach is intended to provide more accurate and context-aware medical reasoning for users who rely on Indic languages, moving beyond the limitations of general-purpose English-centric models.

Note: The provided source material is a preliminary abstract; specific architectural details regarding the agent coordination mechanisms and quantitative performance benchmarks are not detailed in the available text.