IndicMedDialog: A Parallel Multi-Turn Medical Dialogue Dataset for Accessible Healthcare in Indic Languages
Year: 2026
Type: conference-paper
Abstract: 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 characterspacing errors.Building on this dataset, we fine-tune IndicMedLM via parameterefficient 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. " Rec ChatGPT Medical Symptom ConsultationIf you overeated at dinner last night and woke up with fever and cough, the symptoms are more likely due to a mild infection rather than the overeating itself.Following a period of overindulgence at dinner last night, I awoke this morning presenting with a fever and a cough ChatGPT 5.3 PATIENT Figure 1: Response from a general-purpose LLM (ChatGPT).The model produces a single explanatory answer without follow-up questioning or symptom elicitation.Contributions.The main contributions of this work are: • We construct IndicMedDialog, the first parallel multi-turn medical dialogue dataset spanning English and nine Indic languages, with native-speaker verification and scriptaware post-processing for translation quality assurance.• We incorporate patient pre-context (age, gender, allergies, and demographic attributes) to enable personalized multi-turn symptom elicitation, more closely simulating real clinical consultations.• We develop IndicMedLM, a parameterefficiently fine-tuned medical dialogue model deployable on modest hardware, and perform systematic error analysis identifying five failure modes across languages and their clinical risk implications.• We conduct medical expert evaluation to validate the clinical plausibility and safety of the generated diagnostic dialogues.For reproducibility, we release the dataset, model checkpoints, and training code through an GitHub repository 1 .1 https://github.com/ShubhamKumarNigam/ IndicMedDialog " Rec ChatGPT Medical Symptom ConsultationIf you overeated at dinner last night and woke up with fever and cough, the symptoms are more likely due to a mild infection rather than the overeating itself.After overeating at dinner last night, I felt Fever and Cough this morning.ChatGPT 5.
Authors: Shubham Nigam, Suparnojit Sarkar, Piyush Patel
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7166867049