IndicMedDialog: A Parallel Multi-Turn Medical Dialogue Dataset for Accessible Healthcare in Indic Languages
For healthcare AI researchers and practitioners in India, this work provides a realistic multilingual medical dialogue resource and a fine-tuned model, though the approach is incremental (extending existing datasets and fine-tuning a small model).
The authors introduce IndicMedDialog, a parallel multi-turn medical dialogue dataset covering English and nine Indic languages, and fine-tune a small language model (IndicMedLM) for multilingual symptom elicitation. The model outperforms zero-shot baselines across all languages, with expert evaluation confirming clinical plausibility.
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-generated synthetic consultations, translated using TranslateGemma, verified by native speakers, and refined through a script-aware post-processing pipeline to correct phonetic, lexical, and character-spacing errors. Building on this dataset, we fine-tune IndicMedLM via parameter-efficient adaptation of a quantized small language model, incorporating optional patient pre-context to personalise multi-turn symptom elicitation. We evaluate against zero-shot multilingual baselines, conduct systematic error analysis across ten languages, and validate clinical plausibility through medical expert evaluation.