Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine

Summary: Drug-drug interactions are important in drug research and can cause adverse reactions in patients. Computer methods, including identifying known or predicting unknown interactions, have been used to address this issue. This review focuses on the progress of machine learning in predicting unknown drug interactions, discussing databases, methods, and challenges for further research.

Summary: Machine learning models trained on clinician-initiated administrative data show performance close to EMR-based benchmarks for inpatient outcomes, but exhibit declines in performance when dealing with specific patient populations, such as myocardial infarction patients. The results highlight the importance of physician diagnosis in the prognostic performance of these models and suggest that models with similar performance may derive their signal from observing clinical behavior to generate predictions. Performance exceeding these benchmarks is necessary for models to guide clinicians in individual decisions.