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This paper introduces a framework for synthesizing long-term medical dialogue datasets using LLMs, and creates MediLongChat with three benchmark tasks to evaluate healthcare agents' memory and reasoning capabilities. Experiments show that even state-of-the-art LLMs struggle with these tasks.
IndicMedDialog is a parallel multi-turn medical dialogue dataset spanning English and nine Indic languages, with a fine-tuned model for personalized symptom elicitation. The dataset is derived from MDDial, enhanced with LLM-generated synthetic consultations and expert verification, supporting multilingual healthcare AI.