Predicting Adverse Interactions: A Comprehensive Review of AI-Driven Drug-Drug Interaction Models for Enhanced Patient Safety

The complexity of the additive medications in today's clinical practice demands sincere methods in determining the probable drug-drug interactions (DDIs) to ensure the safety of the patients. This research proposal focuses on an assessment of the use of AI models in forecasting DDIs to reduce the impact of adverse effects on patients' results. By using supervised machine learning (ML) techniques, we process large pharmacological datasets to find patterns that are associated with DDIs. The study uses the deep learning method, NLP property, and reinforcement learning processes to analyze complex dependency patterns in drug characteristics and interaction possibilities. Outcome analysis has shown that the AI models have better sensitivity and specificity scores, which confirms the models' superiority over the traditional rule-based systems in predicting DDIs. In addition, with the help of AI algorithms applied to EHR, it is possible to make predictions and obtain the alerts integrated into EHR helping healthcare professionals to make decisions. Therefore, this paper discusses how by developing a firmer groundwork for AI in pharmacovigilance, it is possible to improve patient safety and minimize the number of severe adverse consequences elicited by DDIs in a cost-effective manner. Future work will be mainly on fine-tuning the model for improving the accuracy of the models and further collection of data set to include all possible drug interactions. This is true due to the discovery of the capabilities of AI as shown in the respective fields of medicine with more secure and distinct medical informatics on the horizon.