ArticleNature medicine2024
Large language models for preventing medication direction errors in online pharmacies.
Article in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- An LLM chatbot to facilitate primary-to-specialist care transitions: a randomized controlled trial.Nature medicine · 2026Trial
- Locally Deployed Large Language Models for AI-Assisted Outpatient Prescription Review: Crossover Study.JMIR medical informatics · 2026Article
- Large Language Models in Adverse Drug Reaction Detection and Pharmacovigilance: A Systematic Review of Current Applications, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Ethical Considerations in Personal Health Large Language Models.Journal of medical Internet research · 2026Article
- Hyper-RAG: combating LLM hallucinations using hypergraph-driven retrieval-augmented generation.Nature communications · 2026Article
- The effects of multitype prompt engineering for large language models in hypertension treatment decisions.NPJ digital medicine · 2026Article
- Benchmarking Large Language Models for Drug Combination Alerts: Achieving Expert-Level Reliability via Knowledge Grounding and Contextual Reasoning.Journal of medicinal chemistry · 2026Article
- Scaling medical AI across clinical contexts.Nature medicine · 2026Review
- Assessing the Quality of AI Responses to Patient Concerns About Axial Spondyloarthritis: Delphi-Based Evaluation.JMIR AI · 2026Article
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- Article
- Development of a protocol on safe practices for insulin preparation and administration.Revista brasileira de enfermagem · 2026Article
- The Impact of Bilingual Communities on the Effective Communication Process in Open-Circuit Pharmacies: A Quantitative Study to Overcome Language Barriers in Patient Care from Eastern Region of Romania.Integrated pharmacy research & practice · 2026Article
- Vulnerability of Large Language Models to Prompt Injection When Providing Medical Advice.JAMA network open · 2025Article
- Systematic comparison of GPT models for the analysis of pathology reports in a low-resource language: A case study for Turkish.American journal of clinical pathology · 2025Article
- Large language models in clinical psychiatry: Applications and optimization strategies.World journal of psychiatry · 2025Review
- Large language model as clinical decision support system augments medication safety in 16 clinical specialties.Cell reports. Medicine · 2025Article
- LINS: A general medical Q&A framework for enhancing the quality and credibility of LLM-generated responses.Nature communications · 2025Article
- A scoping review of natural language processing in addressing medically inaccurate information: Errors, misinformation, and hallucination.Journal of biomedical informatics · 2025Article
- A perspective for adapting generalist AI to specialized medical AI applications and their challenges.NPJ digital medicine · 2025Review
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Errors in pharmacy medication directions, such as incorrect instructions for dosage or frequency, can increase patient safety risk substantially by raising the chances of adverse drug events. This study explores how integrating domain knowledge with large language models (LLMs)-capable of sophisticated text interpretation and generation-can reduce these errors. We introduce MEDIC (medication direction copilot), a system that emulates the reasoning of pharmacists by prioritizing precise communication of core clinical components of a prescription, such as dosage and frequency. It fine-tunes a first-generation LLM using 1,000 expert-annotated and augmented directions from Amazon Pharmacy to extract the core components and assembles them into complete directions using pharmacy logic and safety guardrails. We compared MEDIC against two LLM-based benchmarks: one leveraging 1.5 million medication directions and the other using state-of-the-art LLMs. On 1,200 expert-reviewed prescriptions, the two benchmarks respectively recorded 1.51 (confidence interval (CI) 1.03, 2.31) and 4.38 (CI 3.13, 6.64) times more near-miss events-errors caught and corrected before reaching the patient-than MEDIC. Additionally, we tested MEDIC by deploying within the production system of an online pharmacy, and during this experimental period, it reduced near-miss events by 33% (CI 26%, 40%). This study shows that LLMs, with domain expertise and safeguards, improve the accuracy and efficiency of pharmacy operations.
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