ArticleNPJ digital medicine2025
A scoping review on generative AI and large language models in mitigating medication related harm.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Integration of artificial intelligence applications in clinical pharmacy services: A scoping review.Future healthcare journal · 2026Review
- Navigating uncertainty matters: Evaluating large language models for drug-drug interaction identification.Journal of managed care & specialty pharmacy · 2026Article
- Novelty to necessity: large language models in clinical practice-a call to action for physicians.Internal and emergency medicine · 2026Review
- Generative AI and Large Language Models in Rehabilitation: A Scoping Review.Life (Basel, Switzerland) · 2026Review
- Large Language Models in Adverse Drug Reaction Detection and Pharmacovigilance: A Systematic Review of Current Applications, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- PromptSE: drug side effect prediction with LLM-derived pharmacological representations.Scientific reports · 2026Article
- AI-Based Automation for Medication Reconciliation: Scoping Review.Journal of medical Internet research · 2026Article
- Large Language Model Adaptation Strategies in Speech-Based Cognitive Screening: Systematic Evaluation.JMIR AI · 2026Article
- Innovating global regulatory frameworks for generative AI in medical devices is an urgent priority.NPJ digital medicine · 2026Review
- ChatGPT as a Tool for Perioperative Safety: Evaluating Drug Interaction Detection in Anesthetic Regimens.Cureus · 2026Article
- Article
- Multi-view knowledge-guided flow subgraphs with substructure initialization for explainable DDI prediction.Briefings in functional genomics · 2026Article
- Radiomics and deep learning in upper tract urothelial carcinoma: advancing preoperative risk stratification and clinical decision-making.Frontiers in oncology · 2026Review
- Acceptance of generative AI-assisted medical decision-making among Chinese physicians and patients and its ethical determinants: a cross-sectional survey.Frontiers in public health · 2026Article
- Deep learning and generative AI for medical imaging and clinical decision support systems: a structured critical review.Frontiers in digital health · 2026Review
- A comparative study of the performance of different large language models in the Chinese National Pharmacist Licensing Examination.Frontiers in medicine · 2026Article
- Tribulations, Triumphs, and Governance: Shaping the Future of Artificial Intelligence in Healthcare.EJIFCC · 2025Article
- Comparative evaluation of artificial intelligence platforms and drug interaction screening databases using real-world patient data.Exploratory research in clinical and social pharmacy · 2025Article
- Large Language Models in Randomized Controlled Trials Design: Observational Study.Journal of medical Internet research · 2025Observational
- Large Language Models for Adverse Drug Events: A Clinical Perspective.Journal of clinical medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.