MedAidDialog: A Multilingual Multi-Turn Medical Dialogue Dataset for Accessible Healthcare

TL;DR

MedAidDialog introduces a multilingual, multi-turn medical dialogue dataset and a lightweight conversational model, enhancing realistic, accessible, and personalized AI-driven healthcare consultations across seven languages.

Contribution

The paper presents MedAidDialog, a novel multilingual, multi-turn medical dialogue dataset, and MedAidLM, a resource-efficient model for realistic medical conversations in multiple languages.

Findings

Effective symptom elicitation in multi-turn dialogues

Generation of plausible diagnostic recommendations

Positive expert evaluation of dialogue coherence

Abstract

Conversational artificial intelligence has the potential to assist users in preliminary medical consultations, particularly in settings where access to healthcare professionals is limited. However, many existing medical dialogue systems operate in a single-turn question--answering paradigm or rely on template-based datasets, limiting conversational realism and multilingual applicability. In this work, we introduce MedAidDialog, a multilingual multi-turn medical dialogue dataset designed to simulate realistic physician--patient consultations. The dataset extends the MDDial corpus by generating synthetic consultations using large language models and further expands them into a parallel multilingual corpus covering seven languages: English, Hindi, Telugu, Tamil, Bengali, Marathi, and Arabic. Building on this dataset, we develop MedAidLM, a conversational medical model trained using parameter-efficient fine-tuning on quantized small language models, enabling deployment without high-end computational infrastructure. Our framework additionally incorporates optional patient pre-context information (e.g., age, gender, allergies) to personalize the consultation process. Experimental results demonstrate that the proposed system can effectively perform symptom elicitation through multi-turn dialogue and generate diagnostic recommendations. We further conduct medical expert evaluation to assess the plausibility and coherence of the generated consultations.