Language
English
Publication Date
3-28-2025
Journal
npj Digital Medicine
DOI
10.1038/s41746-025-01565-7
PMID
40155703
PMCID
PMC11953325
PubMedCentral® Posted Date
3-28-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Medication-related harm has a significant impact on global healthcare costs and patient outcomes. Generative artificial intelligence (GenAI) and large language models (LLM) have emerged as a promising tool in mitigating risks of medication-related harm. This review evaluates the scope and effectiveness of GenAI and LLM in reducing medication-related harm. We screened 4 databases for literature published from 1st January 2012 to 15th October 2024. A total of 3988 articles were identified, and 30 met the criteria for inclusion into the final review. Generative AI and LLMs were applied in three key applications: drug-drug interaction identification and prediction, clinical decision support, and pharmacovigilance. While the performance and utility of these models varied, they generally showed promise in early identification, classification of adverse drug events, and supporting decision-making for medication management. However, no studies tested these models prospectively, suggesting a need for further investigation into integration and real-world application.
Keywords
Health care, Medical research
Published Open-Access
yes
Recommended Citation
Ong, Jasmine Chiat Ling; Chen, Michael Hao; Ng, Ning; et al., "A Scoping Review on Generative AI and Large Language Models in Mitigating Medication Related Harm" (2025). Faculty, Staff and Students Publications. 8491.
https://digitalcommons.library.tmc.edu/baylor_docs/8491