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DocTalkBN: A Novel Dataset of Expert Telemedicine Conversations in Bengali — arXiv:2608.27110

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.27110
License (as archived): CC0-1.0
Archived: 2026-10-17T15:20:18.049Z
SHA-256 of the archived text: 91c57122d67177e4be28dd2f3a7a15357818021ca9329e8df232a46048b42b82
Stable URL of this page: https://metraipolis.com/documento/dc_4ee892ea506c

How to cite

DocTalkBN: A Novel Dataset of Expert Telemedicine Conversations in Bengali — arXiv:2608.27110 — arXiv (en), https://arxiv.org/abs/2608.27110 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4ee892ea506c (Metraipolis archive dc_4ee892ea506c, sha256 91c57122d67177e4be28dd2f3a7a15357818021ca9329e8df232a46048b42b82)

BibTeX

@misc{dc4ee892ea506c,
  title = {DocTalkBN: A Novel Dataset of Expert Telemedicine Conversations in Bengali — arXiv:2608.27110},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.27110},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4ee892ea506c; sha256 91c57122d67177e4be28dd2f3a7a15357818021ca9329e8df232a46048b42b82},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.27110. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-28 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2608.27110 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T15:15:18.085Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).