arXiv:2605.24635v1 Announce Type: new Abstract: Medical large language models hold promise for reducing healthcare disparities, yet Hindi remains severely underrepresented. While medical LLMs excel in high-resource languages, their performance degrades sharply in Hindi, particularly on Indian systems of medicine. We argue that robust cross-lingual medical transfer requires Hindi reasoning. To this end, we introduce HiMed, a Hindi reasoning medical corpus and benchmark suite covering both Western and Indian medicine. We further propose HiMed-8B, a Hindi-form medical reasoning LLM, through the d
The rapid advancement of LLMs in high-resource languages has created a clear disparity and an urgent need for equivalent capabilities in underrepresented languages like Hindi.
This development addresses a critical gap in healthcare AI, fostering digital equity and enabling the application of advanced medical AI to a significant portion of the global population while including neglected medical systems.
The availability of a Hindi reasoning medical corpus and a specialized LLM for Hindi marks a significant step towards more inclusive and regionally relevant AI medical tools, shifting focus beyond English-centric models.
- · Indian healthcare sector
- · Hindi-speaking populations
- · AI developers focused on multilingual models
- · Developers of Indian traditional medicine
- · Monolingual LLM developers
- · Healthcare systems reliant solely on high-resource language AI
Improved healthcare diagnostics and accessibility for Hindi-speaking regions through AI.
Increased investment and development of AI models for other underrepresented languages and traditional knowledge systems globally.
Potential for new economic opportunities and competitive advantage for nations developing language-specific AI infrastructure tailored to their unique needs.
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