Accessibility settings

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

Viewpoints and Perspectives (1415) E-Health Policy and Health Systems Innovation (532) Natural Language Processing (1297) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (274) Decision Support for Health Professionals (2249) Safety and Error Prevention in Health (322) Pharmacovigilance (245) Artificial Intelligence (4815)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50274, first published .
Woman and robot analyze molecular structure on futuristic screen.

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 (19) Tweetations (6) 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 Artificial Intelligence–Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review. Journal of Medical Internet Research 2025;27:e69678 View
  4. Potter E, Reyes M, Naples J, Dal Pan G. FDA Adverse Event Reporting System (FAERS) Essentials: A Guide to Understanding, Applying, and Interpreting Adverse Event Data Reported to FAERS. Clinical Pharmacology & Therapeutics 2025;118(3):567 View
  5. Janiczak S, Tanveer S, Tom K, Zhang R, Ma Y, Wolf L, Muñoz M. An Evaluation of Duplicate Adverse Event Reports Characteristics in the Food and Drug Administration Adverse Event Reporting System. Drug Safety 2025;48(10):1119 View
  6. Hamad F, Ali M, Kindawi M, Mustafa R, Omer Saeed A, Khalafalla Abdelfadeel W, Ibrahim E. Toward Standardized Performance Metrics in the Cardiovascular ICU: A Systematic Review of Quality Indicators. Cureus 2025 View
  7. Sharma R, Panja S. Addressing Academic Dishonesty in Higher Education: A Systematic Review of Generative AI’s Impact. Open Praxis 2025;17(2) View
  8. Barbieri M, Battini V, Carnovale C, Cocco M, Papoutsi D, Heckmann N, Dong G, Rossi A, Peker S, Van Manen R, Thapar S, Sessa M. Artificial intelligence in pharmacovigilance signal management: a review of tools, implementations, research, and regulatory landscape. Expert Opinion on Drug Safety 2026;25(2):207 View
  9. Velasco L, Wang W. Theoretical appraisal of explanatory paradigms for artificial intelligence usage by medical doctors. DIGITAL HEALTH 2025;11 View
  10. George J, Kumar A, Kalaiselvan V, Shetty V, Nair A, Chakraborty A, Kiran M, Sindu M, Buddha S, Randeo S, Boopathi D, Kumar B, Pradeep T, Arshin A, Amrutha C, Vijayakumar H, Shukla S, Reddy V. Development and validation of a standardized causality assessment tool for adverse events associated with medical devices. Perspectives in Clinical Research 2026 View
  11. Garcia I, Herrera D. AI-Based Pharmacovigilance: A Critical Review of Signal Validity. Pharmacophore 2026;17(3):33 View
  12. Ohta M, Ota M, Ohta M. AI for Causality Assessment in Pharmacovigilance: Protocol for a Scoping Review. JMIR Research Protocols 2026;15:e101691 View
  13. Wu L, Xu J, Dang O, Ball R. Does generative AI mean the “end of history” for pharmacovigilance automation? towards a framework for the future of human-AI systems. Frontiers in Drug Safety and Regulation 2026;6 View
  14. Al-Hinai S, Al-Balushi A, Al-Maskari M, Al-Mahruqi S. An Adverse-Event Provenance Graph for Linking Case Narratives, Exposure Evidence, Clinical Context, Analytical Decisions, and Safety Conclusions. International Journal of Pharmaceutical Research and Allied Sciences 2025;14(4):57 View
  15. Norén G, Durand J, Kara V, MacEntee Pileggi E, Carroll H, Hill S, Hauben M, Botsis T, Rägo L, Altman R, Dogné J, Amelio J, Barrios L, Bate A, Bellur A, Berridge A, Cherkas Y, Cooper S, Diniz M, Domalik D, Franco P, Girod J, Grabowski N, Henn T, Kempf D, Kidos K, Lorenz D, Patel R, Reinhard Pietzsch J, Römming H, Straus W, Whitehead J, Buch B, Egebjerg Juul K, Harrison K, Hirokawa-Voorburg S, Horst A, Jensen M, Kjær J, Ling B, Da Luz Carvalho Soares M, Matsunaga Y, Mentzer D, Messelhäußer M, Moreira Cruz F, Perez N, Scholz I, Stammschulte T, Tregunno P, Mathur R, Meldau E, Norén G, Rosenfeld S, Yau B, Heaton S, Le Louët H, Rannula K, Tsintis P. Artificial Intelligence in Pharmacovigilance: Guiding Principles from the CIOMS Working Group XIV. Drug Safety 2026 View

Books/Policy Documents

  1. Schulman A, Fernández-Torras A, Gadiya Y, Saeed K, Mestres J, Tanoli Z. Applied Artificial Intelligence for Drug Discovery. View
  2. Kwan J, Fritz P, Nguyen T. Handbook of Tissue Reconstruction and Regeneration. View
  3. Kwan J, Fritz P, Nguyen T. Handbook of Tissue Reconstruction and Regeneration. View
  4. Riaz M, Khan H, Faheem M, Al-Harrasi A, Masood M, Munawar N, Zaini P, Rasheed M, Ceasar S, Ahmad A. Intelligent Gene Editing in Plants. View