IndicMedDialog: A Parallel Multi-Turn Medical Dialogue Dataset for Accessible Healthcare in Indic Languages
Summary
IndicMedDialog is a parallel multi-turn medical dialogue dataset spanning English and nine Indic languages, with a fine-tuned model for personalized symptom elicitation. The dataset is derived from MDDial, enhanced with LLM-generated synthetic consultations and expert verification, supporting multilingual healthcare AI.
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Paper page - IndicMedDialog: A Parallel Multi-Turn Medical Dialogue Dataset for Accessible Healthcare in Indic Languages
Source: https://huggingface.co/papers/2605.13292
Abstract
A parallel multi-turn medical dialogue dataset spanning English and nine Indic languages is introduced, along with a fine-tuned model using parameter-efficient adaptation for personalized symptom elicitation.
Most existing medical dialogue systems operate in a single-turn question--answering paradigm or rely on template-based datasets, limiting conversational realism and multilingual applicability. We introduce IndicMedDialog, a parallel multi-turn medical dialogue dataset spanning English and nine Indic languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Punjabi, Tamil, Telugu, and Urdu. The dataset extends MDDial with LLM-generatedsynthetic consultations, translated usingTranslateGemma, verified by native speakers, and refined through a script-awarepost-processing pipelineto correct phonetic, lexical, and character-spacing errors. Building on this dataset, we fine-tune IndicMedLM viaparameter-efficient adaptationof a quantized small language model, incorporating optional patient pre-context to personalise multi-turn symptom elicitation. We evaluate againstzero-shot multilingualbaselines, conductsystematic error analysisacross ten languages, and validateclinical plausibilitythrough medical expert evaluation.
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