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

Theme Issue (2023): Generative & Multimodal AI in Digital Cardiovascular Medicine (8) Machine Learning (2076) Chatbots and Conversational Agents (740) Generative Language Models Including ChatGPT (708) Cardiovascular Medical Education and Training for Health Professionals (11) Cardiology (224) Patient Education in Cardiology (45) Gastroenterology and Hepatology (127) Neurology and Neurosciences (286)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53421, first published .
A Multidisciplinary Assessment of ChatGPT’s Knowledge of Amyloidosis: Observational Study

A Multidisciplinary Assessment of ChatGPT’s Knowledge of Amyloidosis: Observational Study

A Multidisciplinary Assessment of ChatGPT’s Knowledge of Amyloidosis: Observational Study

Authors of this article:

Ryan C King1 Author Orcid Image ;   Jamil S Samaan2 Author Orcid Image ;   Yee Hui Yeo2 Author Orcid Image ;   Yuxin Peng3 Author Orcid Image ;   David C Kunkel4 Author Orcid Image ;   Ali A Habib5 Author Orcid Image ;   Roxana Ghashghaei1 Author Orcid Image
Article Authors Cited by (9) Tweetations (4) Metrics

Journals

  1. McBee J, Han D, Liu L, Ma L, Adjeroh D, Xu D, Hu G. Assessing ChatGPT’s Competency in Addressing Interdisciplinary Inquiries on Chatbot Uses in Sports Rehabilitation: Simulation Study. JMIR Medical Education 2024;10:e51157 View
  2. Aydin S, Karabacak M, Vlachos V, Margetis K. Large language models in patient education: a scoping review of applications in medicine. Frontiers in Medicine 2024;11 View
  3. Ulug E, Gunesli I, Acıkgoz Pinar A, Yildiz B. Evaluating reliability, quality, and readability of ChatGPT's nutritional recommendations for women with polycystic ovary syndrome. Nutrition Research 2025;133:46 View
  4. Samaan J, Issokson K, Feldman E, Fasulo C, Rajeev N, Ng W, Hollander B, Yeo Y, Vasiliauskas E. Examining the Accuracy and Reproducibility of Responses to Nutrition Questions Related to Inflammatory Bowel Disease by Generative Pre-trained Transformer-4. Crohn's & Colitis 360 2024;7(1) View
  5. Wang L, Li J, Zhuang B, Huang S, Fang M, Wang C, Li W, Zhang M, Gong S. Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis. Journal of Medical Internet Research 2025;27:e64486 View
  6. King R, Samaan J, Haquang J, Bharani V, Margolis S, Srinivasan N, Peng Y, Yeo Y, Ghashghaei R. Improving the Readability of Institutional Heart Failure–Related Patient Education Materials Using GPT-4: Observational Study. JMIR Cardio 2025;9:e68817 View
  7. Wang S, Gao H, Lin P, Qian T, Du Y, Xu L. Evaluating chat generative pretrained transformer in answering questions on endoscopic mucosal resection and endoscopic submucosal dissection. World Journal of Gastrointestinal Oncology 2025;17(10) View
  8. Srinivasan N, Samaan J, Rajeev N, Premkumar A, Alvarez K, Harvey L, Nguyen S, Tran A, Samakar K. Perspectives of Patients Seeking Bariatric Surgery: The Impact of Early Patient-Provider Communication on Bariatric Surgery Utilization. Obesity Surgery 2025;35(11):4639 View
  9. Tarhan M, Sahin Ozdemir M. Comparison of the accuracy and reliability of ChatGPT-4o and Gemini in answering HIV-related questions. BMC Infectious Diseases 2025;25(1) View