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AI Research
arXiv CS.AI·Jun 12·5 min read
ArogyaSutra: A Multi-Agent Framework for Multimodal Medical Reasoning in Indic Languages
TL;DR
In rural India, patients often face challenges in accessing healthcare due to language barriers and the complexity of medical queries. ArogyaSutra, a multi-agent framework, was developed to enhance multimodal medical reasoning in Indic languages by integrating a large-scale multilingual dataset and actor-critic decision-making.
✦ Why It Matters
Engineers can leverage ArogyaSutra to build AI healthcare solutions that support multiple languages and modalities.
Key Takeaways
- Explore ArogyaBodha for multilingual medical datasets.
- Implement actor-critic methods for decision-making in AI systems.
- Focus on low-resource language support in AI applications.
- Utilize dual-memory mechanisms for enhanced reasoning capabilities.
Full Summary
Multimodal Large Language Models (MLLMs) have shown potential in general reasoning but struggle in specialized fields like healthcare, particularly in multilingual and low-resource contexts. ArogyaBodha, a comprehensive dataset, was created from diverse sources, encompassing 31 body systems and 21 clinical domains in English and seven major Indian languages.
ArogyaSutra, the proposed framework, employs an actor-critic approach, which is a reinforcement learning method, to facilitate reasoning-aware decision-making through dual-memory mechanisms. The framework also utilizes stored simulation trajectories for knowledge distillation, enhancing learning efficiency.
Experimental results indicated significant improvements in multilingual medical reasoning accuracy, with all Indic languages benefiting from the new dataset and framework. Ablation studies confirmed the effectiveness of each component in the system.
This work highlights the importance of developing AI tools that cater to diverse linguistic and cultural contexts in healthcare.