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

Theme Issue: ChatGPT and Generative Language Models in Medical Education (84) e-Learning and Medical Education (691) New Resources for Medical Education (68) Machine Learning (1465) Testing and Assessment in Medical Education (52) Chatbots and Conversational Agents (531) Generative Language Models Including ChatGPT (311)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50965, first published .
Comparison of the Performance of GPT-3.5 and GPT-4 With That of Medical Students on the Written German Medical Licensing Examination: Observational Study

Comparison of the Performance of GPT-3.5 and GPT-4 With That of Medical Students on the Written German Medical Licensing Examination: Observational Study

Comparison of the Performance of GPT-3.5 and GPT-4 With That of Medical Students on the Written German Medical Licensing Examination: Observational Study

Authors of this article:

Annika Meyer1 Author Orcid Image ;   Janik Riese2 Author Orcid Image ;   Thomas Streichert1 Author Orcid Image
Article Authors Cited by (15) Tweetations (3) Metrics

Journals

  1. Miao J, Thongprayoon C, Suppadungsuk S, Garcia Valencia O, Cheungpasitporn W. Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications. Medicina 2024;60(3):445 View
  2. Lucas F, Mackie I, d'Onofrio G, Frater J. Responsible use of chatbots to advance the laboratory hematology scientific literature: Challenges and opportunities. International Journal of Laboratory Hematology 2024;46(S1):9 View
  3. Zhu L, Mou W, Hong C, Yang T, Lai Y, Qi C, Lin A, Zhang J, Luo P. The Evaluation of Generative AI Should Include Repetition to Assess Stability. JMIR mHealth and uHealth 2024;12:e57978 View
  4. Meyer A, Ruthard J, Streichert T. Dear ChatGPT – can you teach me how to program an app for laboratory medicine?. Journal of Laboratory Medicine 2024;48(5):197 View
  5. Kaneda Y, Tayuinosho A, Tomoyose R, Takita M, Hamaki T, Tanimoto T, Ozaki A. Evaluating ChatGPT's effectiveness and tendencies in Japanese internal medicine. Journal of Evaluation in Clinical Practice 2024;30(6):1017 View
  6. Lee T, Rao A, Campbell D, Radfar N, Dayal M, Khrais A. Evaluating ChatGPT-3.5 and ChatGPT-4.0 Responses on Hyperlipidemia for Patient Education. Cureus 2024 View
  7. Meyer A, Soleman A, Riese J, Streichert T. Comparison of ChatGPT, Gemini, and Le Chat with physician interpretations of medical laboratory questions from an online health forum. Clinical Chemistry and Laboratory Medicine (CCLM) 2024;62(12):2425 View
  8. Liu M, Okuhara T, Chang X, Shirabe R, Nishiie Y, Okada H, Kiuchi T. Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2024;26:e60807 View
  9. Suwała S, Szulc P, Guzowski C, Kamińska B, Dorobiała J, Wojciechowska K, Berska M, Kubicka O, Kosturkiewicz O, Kosztulska B, Rajewska A, Junik R. ChatGPT-3.5 passes Poland’s medical final examination—Is it possible for ChatGPT to become a doctor in Poland?. SAGE Open Medicine 2024;12 View
  10. 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
  11. Brandtzaeg P, Skjuve M, Følstad A. Understanding model power in social AI. AI & SOCIETY 2024 View
  12. Kipp M. From GPT-3.5 to GPT-4.o: A Leap in AI’s Medical Exam Performance. Information 2024;15(9):543 View
  13. Brin D, Sorin V, Konen E, Nadkarni G, Glicksberg B, Klang E. How GPT models perform on the United States medical licensing examination: a systematic review. Discover Applied Sciences 2024;6(10) View
  14. Fan K, Fan K. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato 2024;4(4):124 View
  15. Pillai J, Pillai K. ChatGPT as a medical education resource in cardiology: Mitigating replicability challenges and optimizing model performance. Current Problems in Cardiology 2024;49(12):102879 View