Medication errors are a serious problem, leading to adverse drug events and huge costs for healthcare systems. Traditional systems for preventing these errors, known as Clinical Decision Support Systems (CDSSs), often rely on outdated databases and rigid rules, leading to inaccurate alerts and 'alert fatigue' among doctors. But what if AI could offer a solution? Researchers have developed a groundbreaking CDSS called HELIOT that uses the power of large language models (LLMs), like the technology behind ChatGPT, combined with a comprehensive drug database. HELIOT can interpret complex medical texts, including drug leaflets and patient records, and synthesize information to provide personalized recommendations. In tests using a simulated patient dataset, HELIOT achieved 100% accuracy in identifying potential drug allergies. This exciting development offers a potential game-changer in drug safety, paving the way for more accurate, personalized, and efficient allergy management. The future of this technology looks promising, with plans to expand testing with real-world data and further optimize its performance for even faster responses in critical clinical settings. This means fewer medication errors, safer prescriptions, and a significant reduction in healthcare costs. The research team also plans to refine HELIOT’s ability to manage complex drug interactions and personalize recommendations based on individual patient factors, like genetics and lifestyle.