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

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.

Figures (3)

• Figure 1: Example response from a general-purpose LLM (ChatGPT 5.3). The model produces a single explanatory response without collecting additional symptoms or conducting follow-up questioning.
• Figure 2: Example interaction with MedAidLM. The system first incorporates patient pre-context information (e.g., age, gender, and allergies) and then performs multi-turn dialogue to collect symptoms before producing a diagnostic recommendation.
• Figure 3: Overview of the proposed framework. Stage 1: Data Augmentation. The MDDial dataset is expanded with synthetic medical dialogues, followed by coherence and diversity filtering. Stage 2: Model Adaptation. Compact open-source language models are fine-tuned using parameter-efficient training and LoRA-based SFT. The dotted connection indicates an optional GRPO optimisation stage applied to selected models. Stage 3: Deployment. The best-performing checkpoint is deployed as MedAidLM, which operates within a multilingual inference loop that incorporates optional patient pre-context and bidirectional translation.