MedAidDialog: A Multilingual Multi-Turn Medical Dialogue Dataset for Accessible Healthcare
This work addresses the problem of limited conversational realism and multilingual applicability in medical AI for underserved populations, though it is incremental as it builds on existing datasets and methods.
The authors tackled the lack of realistic and multilingual medical dialogue datasets by introducing MedAidDialog, a synthetic dataset covering seven languages, and developed MedAidLM, a model that effectively performs symptom elicitation and generates diagnostic recommendations, with expert evaluation confirming plausibility and coherence.
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.