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

Theme Issue: ChatGPT and Generative Language Models in Medical Education (98) Testing and Assessment in Medical Education (69) Chatbots and Conversational Agents (617) Generative Language Models Including ChatGPT (467)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55048, first published .
Exploring the Performance of ChatGPT Versions 3.5, 4, and 4 With Vision in the Chilean Medical Licensing Examination: Observational Study

Exploring the Performance of ChatGPT Versions 3.5, 4, and 4 With Vision in the Chilean Medical Licensing Examination: Observational Study

Exploring the Performance of ChatGPT Versions 3.5, 4, and 4 With Vision in the Chilean Medical Licensing Examination: Observational Study

Authors of this article:

Marcos Rojas1 Author Orcid Image ;   Marcelo Rojas2 Author Orcid Image ;   Valentina Burgess2 Author Orcid Image ;   Javier Toro-Pérez2 Author Orcid Image ;   Shima Salehi1 Author Orcid Image
Article Authors Cited by (11) Tweetations (5) Metrics

Journals

  1. 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
  2. Tong W, Zhang X, Zeng H, Pan J, Gong C, Zhang H. Reforming China’s Secondary Vocational Medical Education: Adapting to the Challenges and Opportunities of the AI Era. JMIR Medical Education 2024;10:e48594 View
  3. Kipp M. From GPT-3.5 to GPT-4.o: A Leap in AI’s Medical Exam Performance. Information 2024;15(9):543 View
  4. LEVENTOGLU E, SORAN M. Clinical Characteristics of Children with Acute Post-Streptococcal Glomerulonephritis and Re-Evaluation of Patients with Artificial Intelligence. Medeniyet Medical Journal 2024 View
  5. Nakaura T, Yoshida N, Kobayashi N, Nagayama Y, Uetani H, Kidoh M, Oda S, Funama Y, Hirai T. Performance of Multimodal Large Language Models in Japanese Diagnostic Radiology Board Examinations (2021-2023). Academic Radiology 2025;32(5):2394 View
  6. Kim J, Vajravelu B. Assessing the Current Limitations of Large Language Models in Advancing Health Care Education. JMIR Formative Research 2025;9:e51319 View
  7. Qiu Y, Liu C. Capable exam-taker and question-generator: the dual role of generative AI in medical education assessment. Global Medical Education 2025 View
  8. Nguyen H, Dang H, Nguyen T, Hoang V, Nguyen V, Wu J. Accuracy of latest large language models in answering multiple choice questions in dentistry: A comparative study. PLOS ONE 2025;20(1):e0317423 View
  9. Zhao Q, Wang H, Wang R, Cao H. Deriving insights from enhanced accuracy: Leveraging prompt engineering in custom GPT for assessing Chinese Nursing Licensing Exam. Nurse Education in Practice 2025;84:104284 View
  10. Wang J, Shue K, Liu L, Hu G. Preliminary evaluation of ChatGPT model iterations in emergency department diagnostics. Scientific Reports 2025;15(1) View
  11. Rodrigues Alessi M, Gomes H, Contin de Oliveira G, Lopes de Castro M, Grenteski F, Miyashiro L, do Valle Pavanelo C, Tozzini Tavres da Silva L, Terumy Okamoto C. Comparative Performance of Medical Students, ChatGPT-3.5 and ChatGPT-4.0 in Answering Questions from a Brazilian National Medical Exam: An Observational Study (Preprint). JMIR AI 2024 View