DocTalkBN: A Novel Dataset of Expert Telemedicine Conversations in Bengali

2026-08-27

AI Summary

为了解决低资源语言医学对话数据稀缺问题，研究者构建了一个包含557.63小时孟加拉语真实医患交流的多模态数据集DocTalkBN，并设计了三项下游任务来支持基准测试。

Abstract

Reliable medical conversational AI requires authentic expert--patient interaction data, yet such datasets remain scarce, especially for low-resource languages such as Bengali. We present DocTalkBN, a large-scale multimodal dataset of real-world expert telemedicine conversations in Bengali, collected from nationally broadcast telemedicine programs featuring board-certified physicians. DocTalkBN contains 557.63 hours of paired audio and text, 1,515 multi-turn patient calls, 10,274 host--doctor question--answer exchanges, totaling 1.7M tokens, spanning 26 medical specialties. Unlike prior resources derived from medical forums, written health content, or synthetic data, our dataset preserves the spontaneity, contextual richness, and spoken characteristics of authentic medical interactions in a low-resource setting. To support benchmark-driven research, we further construct three downstream tasks from the corpus, medical triage classification, advice safety evaluation, and medical named entity recognition, and benchmark a diverse set of large language models and encoder-based baselines. Our results show that DocTalkBN is a practically useful resource, particularly for clinically grounded reasoning tasks. We release this resource to facilitate future research on reliable medical NLP and safer, more culturally grounded healthcare systems for low-resource languages. Our source codes and dataset are publicly available at https://anonymous.4open.science/r/doctalk.

Problem

• medical conversational AI
• authentic expert-patient interaction data
• low-resource languages
• Bengali

Innovation

• multimodal dataset
• telemedicine conversations
• low-resource language
• medical specialties
• downstream tasks