AI-Powered Prescription Error Detection Using Large Language Models (LLMs): A Systematic Review and Future Perspectives

K. Tulasi Krishna Kumar¹, Koyya Gowtham Reddy² , K. Pujitha Reddy3

¹Associate Professor & Training and Placement Officer, ² B.Pharmacy, 3 BCA, Data Science

tulasikrishnakumar@gmail.com, gowthamreddy290@gmail.com

Abstract
Medication errors remain among the most significant preventable causes of patient harm worldwide, contributing to increased morbidity, mortality, prolonged hospitalization, and escalating healthcare expenditure. Large Language Models (LLMs) — including GPT-4, Gemini, Claude, and Llama — have emerged as promising clinical decision-support tools capable of interpreting complex medical terminology, analyzing prescriptions in real time, and flagging potential errors before medications reach the patient. This review synthesizes current evidence on the application of LLMs in prescription error detection, presents a consolidated system architecture and operational workflow for LLM-enabled medication safety pipelines, and critically examines their benefits, limitations, and future trajectory. Evidence from recent clinical evaluations indicates that LLM-based decision-support tools can achieve high concordance with expert pharmacist judgment and measurably reduce near-miss medication events when deployed with appropriate safeguards. However, challenges including AI hallucination, data privacy, algorithmic bias, regulatory ambiguity, and the continued necessity of human oversight must be addressed before widespread clinical adoption. The review concludes that LLMs hold substantial promise as complementary — rather than autonomous — decision-support systems capable of transforming medication safety and pharmacy practice.

Keywords: Large Language Models; Prescription Error Detection; Medication Safety; Clinical Decision Support; Artificial Intelligence in Healthcare; Electronic Health Records; Pharmacovigilance