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

Viewpoints and Perspectives (550) E-Health Policy and Health Systems Innovation (265) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (171) Artificial Intelligence (1603) Decision Support for Health Professionals (1288) Safety and Error Prevention in Health (216) Pharmacovigilance (150) Natural Language Processing (764)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50274, first published .
Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration

Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration

Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration

Authors of this article:

Robert Ball1 Author Orcid Image ;   Andrew H Talal2 Author Orcid Image ;   Oanh Dang1 Author Orcid Image ;   Monica Muñoz1 Author Orcid Image ;   Marianthi Markatou3 Author Orcid Image
Article Authors Cited by (3) Tweetations (5) Metrics

Journals

  1. Shamim M, Shamim M, Arora P, Dwivedi P. Artificial intelligence and big data for pharmacovigilance and patient safety. Journal of Medicine, Surgery, and Public Health 2024;3:100139 View
  2. Sisó S, Kavirayani A, Couto S, Stierstorfer B, Mohanan S, Morel C, Marella M, Bangari D, Clark E, Schwartz A, Carreira V. Trends and Challenges of the Modern Pathology Laboratory for Biopharmaceutical Research Excellence. Toxicologic Pathology 2025;53(1):5 View
  3. Tun H, Rahman H, Naing L, Malik O. Trust in AI-Based Clinical Decision Support Systems Among Healthcare Workers: A Systematic Review (Preprint). Journal of Medical Internet Research 2024 View