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

Theme Issue: ChatGPT and Generative Language Models in Medical Education (86) Natural Language Processing (706) Ontologies, Classifications, and Coding (243) Chatbots and Conversational Agents (543) Artificial Intelligence (1421) Generative Language Models Including ChatGPT (325)

Published on in Vol 9 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48904, first published .
Differentiating ChatGPT-Generated and Human-Written Medical Texts: Quantitative Study

Differentiating ChatGPT-Generated and Human-Written Medical Texts: Quantitative Study

Differentiating ChatGPT-Generated and Human-Written Medical Texts: Quantitative Study

Authors of this article:

Wenxiong Liao1 Author Orcid Image ;   Zhengliang Liu2 Author Orcid Image ;   Haixing Dai2 Author Orcid Image ;   Shaochen Xu2 Author Orcid Image ;   Zihao Wu2 Author Orcid Image ;   Yiyang Zhang1 Author Orcid Image ;   Xiaoke Huang1 Author Orcid Image ;   Dajiang Zhu3 Author Orcid Image ;   Hongmin Cai1 Author Orcid Image ;   Quanzheng Li4 Author Orcid Image ;   Tianming Liu2 Author Orcid Image ;   Xiang Li4 Author Orcid Image
Article Authors Cited by (16) Tweetations (12) Metrics

Journals

  1. Oliński M, Krukowski K, Sieciński K. Bibliometric Overview of ChatGPT: New Perspectives in Social Sciences. Publications 2024;12(1):9 View
  2. Litake O, Park B, Tully J, Gabriel R. Constructing synthetic datasets with generative artificial intelligence to train large language models to classify acute renal failure from clinical notes. Journal of the American Medical Informatics Association 2024;31(6):1404 View
  3. Chen J, Reddy A, Al-Sharif E, Shoji M, Kalaw F, Eslani M, Lang P, Arya M, Koretz Z, Bolo K, Arnett J, Roginiel A, Do J, Robbins S, Camp A, Scott N, Rudell J, Weinreb R, Baxter S, Granet D. Analysis of ChatGPT Responses to Ophthalmic Cases: Can ChatGPT Think Like an Ophthalmologist?. Ophthalmology Science 2024:100600 View
  4. Kim S. Research ethics and issues regarding the use of ChatGPT-like artificial intelligence platforms by authors and reviewers: a narrative review. Science Editing 2024;11(2):96 View
  5. Park S, Han K, Lee J. Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology. La radiologia medica 2024;129(11):1644 View
  6. van Buchem M, Kant I, King L, Kazmaier J, Steyerberg E, Bauer M. Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study. JMIR AI 2024;3:e60020 View
  7. Alhayan F, Himdi H. Ensemble learning approach for distinguishing human and computer-generated Arabic reviews. PeerJ Computer Science 2024;10:e2345 View
  8. Wang C. Human or Algorithm? The Visual Turing Test of AI-Generated Images. Journal of Multimedia Information System 2024;11(3):201 View
  9. Yang S, Chen S, Zhu H, Lin J, Wang X. A comparative study of thematic choices and thematic progression patterns in human-written and AI-generated texts. System 2024;126:103494 View
  10. Kayabaşı M, Köksaldı S, Durmaz Engin C. Evaluating the reliability of the responses of large language models to keratoconus-related questions. Clinical and Experimental Optometry 2024:1 View
  11. Phillips V, Kiryakoza F, Arefin S, Choudhary N, Garifullin R. ChatGPT versus Google Gemini: a comparison to evaluate patient education guide created on common neurological disorders. Discover Artificial Intelligence 2024;4(1) View

Books/Policy Documents

  1. Xu H, Xue T, Liu D, Zhang F, Westin C, Kikinis R, O’Donnell L, Cai W. Foundation Models for General Medical AI. View