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

AI Language Models in Health Care (190) Natural Language Processing (898) Machine Learning (2040) Ethics, Privacy, and Legal Issues (611) New Technologies (106)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68139, first published .
Transforming Informed Consent Generation Using Large Language Models: Mixed Methods Study

Transforming Informed Consent Generation Using Large Language Models: Mixed Methods Study

Transforming Informed Consent Generation Using Large Language Models: Mixed Methods Study

Authors of this article:

Qiming Shi1 Author Orcid Image ;   Katherine Luzuriaga1 Author Orcid Image ;   Jeroan J Allison2 Author Orcid Image ;   Asil Oztekin3 Author Orcid Image ;   Jamie M Faro2 Author Orcid Image ;   Joy L Lee2 Author Orcid Image ;   Nathaniel Hafer1 Author Orcid Image ;   Margaret McManus1 Author Orcid Image ;   Adrian H Zai1, 2 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

Journals

  1. Türker Kader İ, Arıcan B. Comparing the readability of human- and AI-written informed consent forms for provisional dental restorations. Journal of Health Sciences and Medicine 2025;8(4):697 View
  2. Riaz I, Khan M, Osterman T. Artificial intelligence across the cancer care continuum. Cancer 2025;131(16) View
  3. Wang Z, Gao J, Danek B, Theodorou B, Shaik R, Thati S, Won S, Sun J. Compliance and factuality of large language models for clinical research document generation. Journal of the American Medical Informatics Association 2025 View
  4. Petrou E, Ormond K, Stammbach D, Ash E, Buchholz O, Vayena E. Evaluating GPT-4’s ability to generate informed consent material for genetic testing. npj Artificial Intelligence 2025;1(1) View