This paper is in the following e-collection/theme issue:

Virtual Patients (52) Generative Language Models Including ChatGPT (325) Artificial Intelligence (1421) Theme Issue: ChatGPT and Generative Language Models in Medical Education (86) Simulation (105)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53961, first published .
A Generative Pretrained Transformer (GPT)–Powered Chatbot as a Simulated Patient to Practice History Taking: Prospective, Mixed Methods Study

A Generative Pretrained Transformer (GPT)–Powered Chatbot as a Simulated Patient to Practice History Taking: Prospective, Mixed Methods Study

A Generative Pretrained Transformer (GPT)–Powered Chatbot as a Simulated Patient to Practice History Taking: Prospective, Mixed Methods Study

Authors of this article:

Friederike Holderried1 Author Orcid Image ;   Christian Stegemann–Philipps1 Author Orcid Image ;   Lea Herschbach1 Author Orcid Image ;   Julia-Astrid Moldt1 Author Orcid Image ;   Andrew Nevins2 Author Orcid Image ;   Jan Griewatz1 Author Orcid Image ;   Martin Holderried3 Author Orcid Image ;   Anne Herrmann-Werner1 Author Orcid Image ;   Teresa Festl-Wietek1 Author Orcid Image ;   Moritz Mahling1, 4 Author Orcid Image
Article Authors Cited by (11) Tweetations (3) Metrics

Journals

  1. Holderried F, Stegemann-Philipps C, Herrmann-Werner A, Festl-Wietek T, Holderried M, Eickhoff C, Mahling M. A Language Model–Powered Simulated Patient With Automated Feedback for History Taking: Prospective Study. JMIR Medical Education 2024;10:e59213 View
  2. Huisman T, Huisman T. Artificial Intelligence in Newborn Medicine. Newborn 2024;3(2):96 View
  3. Nicikowski J, Szczepański M, Miedziaszczyk M, Kudliński B. The potential of ChatGPT in medicine: an example analysis of nephrology specialty exams in Poland. Clinical Kidney Journal 2024;17(8) View
  4. Thomae A, Witt C, Barth J. Integration of ChatGPT Into a Course for Medical Students: Explorative Study on Teaching Scenarios, Students’ Perception, and Applications. JMIR Medical Education 2024;10:e50545 View
  5. Borg A, Jobs B, Huss V, Gentline C, Espinosa F, Ruiz M, Edelbring S, Georg C, Skantze G, Parodis I. Enhancing clinical reasoning skills for medical students: a qualitative comparison of LLM-powered social robotic versus computer-based virtual patients within rheumatology. Rheumatology International 2024;44(12):3041 View
  6. Brügge E, Ricchizzi S, Arenbeck M, Keller M, Schur L, Stummer W, Holling M, Lu M, Darici D. Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial. BMC Medical Education 2024;24(1) View

Books/Policy Documents

  1. Pears M, Konstantinidis S. Disruptive Technologies in Education and Workforce Development. View
  2. Triplett W. Pioneering Paradigms in Organizational Research and Consulting Interventions. View
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  4. Solez K, Mahal H, Alam A, Farris A, Levine D, Thennakoonwela P. Transformations of Medical Education and Practice Impacting Critical Care in the New Millennium. View