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

AI in Older Adult Care (209) Natural Language Processing (1225) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (261) Chatbots and Conversational Agents (1134) Artificial Intelligence (4533) Generative Language Models Including ChatGPT (1418) AI Language Models in Health Care (694)

Published on in Vol 8 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69504, first published .
Doctor discusses medication with an elderly couple during a consultation.

Identifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study

Identifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study

Authors of this article:

Vimig Socrates1, 2 Author Orcid Image ;   Donald S Wright3, 4 Author Orcid Image ;   Thomas Huang3 Author Orcid Image ;   Soraya Fereydooni3 Author Orcid Image ;   Christine Dien2 Author Orcid Image ;   Ling Chi3 Author Orcid Image ;   Jesse Albano5 Author Orcid Image ;   Brian Patterson6 Author Orcid Image ;   Naga Sasidhar Kanaparthy3, 4 Author Orcid Image ;   Catherine X Wright7 Author Orcid Image ;   Andrew Loza1, 3 Author Orcid Image ;   David Chartash1, 8 Author Orcid Image ;   Mark Iscoe1, 3 Author Orcid Image ;   Richard Andrew Taylor1, 3, 9 Author Orcid Image
Article Authors Cited by (8) Tweetations Metrics

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  1. Chua H, Nguyen T. Reducing Polypharmacy and Patient Harm: Use of Medication Screening Tools Supports the Detection and Prevention of Adverse Drug Events. The Journal for Nurse Practitioners 2025;21(9):105490 View
  2. Al Meslamani A, Abou Hajal A. Language models for drug–drug interactions: current applications, pitfalls, and future directions. Expert Opinion on Drug Metabolism & Toxicology 2025;21(9):1083 View
  3. Misra S, Das R, Mohapatra S, Dey A. Knowledge, attitude, and practice of dentists in India regarding geriatric oral healthcare and polypharmacy management: a cross-sectional study. BMC Geriatrics 2026;26(1) View
  4. Fine Smilovich E, Kalsy M, Wozneak K, Syed Q, Solberg L. Leveraging AI to Advance Age-Friendly Care in the Veterans Health Administration. JMIR Aging 2026;9:e75686 View
  5. Tabja Bortesi J, Becerra M, Ranisau J, Wen B, Nadesan P, Devereaux P, McGillion M, Petch J. AI-Based Automation for Medication Reconciliation: Scoping Review. Journal of Medical Internet Research 2026;28:e86760 View
  6. Hsieh T, Eriksson C, Meckler G, Hansen M, Bahr N, Bedrick S, Trujillo D, Kim K, Cho B, Zhang K, Jiang X, Guise J. RescueGPT: An Automated System for Detecting Adverse Safety Events in Prehospital Emergency Medical Service Notes With a Zero‐Shot Approach With Large Language Models: A Proof‐of‐Concept Study. Learning Health Systems 2026;10(S1) View
  7. Wu Y, Chen T, Hochberg I, Sun Z, Edry R, Huang Z, Peleg M. Beyond Accuracy: Safety-Centered guidelines for the evaluation of LLM-based therapy recommendation systems for chronic multimorbidity patients. Journal of Biomedical Informatics 2026;180:105072 View
  8. van Zanten S, Boel T, de Jong J, Koomen E, Bais B, Dara A, Giele F, Geertsma C, Sutton R, Scheffer M, Groot J, Lange F. Diagnostic performance of artificial intelligence in the syncope unit. Exploration of Cardiology 2026;4 View