Enhancing Medication Safety with Large Language Models: Advanced Detection and Prediction of Drug-Drug Interactions

Poly-pharmacy means the use of multiple medications for multiple Diseases, with impact to the increases of the risk of drug-drug interactions (DDIs), and it may cause a threat to patient safety. Traditional DDI detection methods are often manual and leads to errors. This study investigates the potential of large language models (LLMs) to improve the efficiency of personalized DDI prediction and to use the AI advancements. By using LLMs’ natural language processing capabilities, we will develop a system that analyzes comprehensive patient data, including medical history, and individual characteristics. The system aims to enabling healthcare providers to make informed decisions and improve the treatment plans. Initial results, while promising, highlight the need for further refinement and larger datasets to improve prediction accuracy. However, this research demonstrates the significant potential of LLM-based systems in transforming medication safety, optimizing treatment regimens, and ultimately enhancing patient care and treatment process.