AI-Powered Predictive Models for Rapid Detection of Novel Drug-Drug Interactions in Polypharmacy Patients

Abstract

Polypharmacy, which is the usage of multiple drugs concomitantly, is highly dangerous due to adverse drug-drug interactions (DDIs) especially in vulnerable groups of patients. The conventional pharmacovigilance techniques are not always capable of identifying new or uncommon DDIs in the shortest time possible, which makes novel approaches to drug safety a necessity. Artificial intelligence (AI) has become an influential tool that can be used to combine various types of data, such as electronic health records, molecular databases, and post-marketing surveillance reports, to forecast and detect the obscured types of interactions with utmost accuracy. Artificial intelligence (AI) predictive models provide disruptive potential in clinical pharmacy, providing opportunities to apply real-time risk stratification, prescribing practice optimization, and prevent adverse events. The models are also helpful in drug discovery, development and repurposing development since it hastens the initial recognition of safety cues. Although there are various obstacles to consider concerning data quality, transparency, and ethical issues, AI-based systems are a way to move towards safer and more individualized medication management. AI can transform the future of clinical practice and pharmacovigilance by increasing the detection and management of DDIs in patients with polypharmacy.