ArticleScientific data2022
A dataset of simulated patient-physician medical interviews with a focus on respiratory cases.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Development of a benchmarking dataset for symptom detection using large language models.JAMIA open · 2026Article
- Scaling Multimodal Agentic AI in Medical Education: Multisite Cross-Sectional Study of Simulation Effectiveness in Primary Care.JMIR formative research · 2026Article
- Application of large language models in medical interview training: a study with medical students.BMC medical education · 2026Article
- Cross-model disagreement as a reference-free signal for prioritizing human review in medical speech transcription.Frontiers in artificial intelligence · 2026Article
- Ten tips to harnessing generative AI for high-quality MCQS in medical education assessment.Medical education online · 2025Article
- Artificial intelligence in undergraduate medical education: an updated scoping review.BMC medical education · 2025Article
- A Comprehensive Polish Medical Speech Dataset for Enhancing Automatic Medical Dictation.Scientific data · 2025Article
- Toward Automated Clinical Transcriptions.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- PodGPT: an audio-augmented large language model for research and education.npj biomedical innovations · 2025Article
- A dataset of simulated patient-physician medical interviews with a focus on respiratory cases.Scientific data · 2022Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) is playing a major role in medical education, diagnosis, and outbreak detection through Natural Language Processing (NLP), machine learning models and deep learning tools. However, in order to train AI to facilitate these medical fields, well-documented and accurate medical conversations are needed. The dataset presented covers a series of medical conversations in the format of Objective Structured Clinical Examinations (OSCE), with a focus on respiratory cases in audio format and corresponding text documents. These cases were simulated, recorded, transcribed, and manually corrected with the underlying aim of providing a comprehensive set of medical conversation data to the academic and industry community. Potential applications include speech recognition detection for speech-to-text errors, training NLP models to extract symptoms, detecting diseases, or for educational purposes, including training an avatar to converse with healthcare professional students as a standardized patient during clinical examinations. The application opportunities for the presented dataset are vast, given that this calibre of data is difficult to access and costly to develop.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.