Abstract

Background:

Drug-drug interactions (DDIs) pose significant risks to patient safety, necessitating accurate detection and management strategies. Traditional methods of identifying DDIs rely on structured drug databases and clinical expertise, which can be time-consuming and prone to missing novel interactions. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) have transformed DDI prediction and management by leveraging big data, real-world evidence, and predictive modeling.

Objective:

This review explores the role of AI-driven technologies in detecting, predicting, and managing DDIs within clinical decision support systems (CDSS). It highlights key AI methodologies, their integration into electronic health records (EHRs), benefits, challenges, and future research directions.

Methods:

A comprehensive literature review was conducted using peer-reviewed journal articles and AI-based DDI studies. The analysis focuses on machine learning models, deep learning techniques, NLP algorithms, and graph-based AI approaches used for DDI prediction.

Results:

AI-based DDI prediction models demonstrate higher accuracy and efficiency compared to traditional methods. Machine learning algorithms enhance DDI detection by identifying complex drug relationships. NLP-driven AI tools extract DDI insights from clinical literature, drug labels, and EHRs to improve real-time risk assessment. AI-powered CDSS integrate patient-specific factors (genetics, comorbidities, lab results) to minimize alert fatigue and improve medication safety. The adoption of federated learning models and predictive toxicology AI enhances personalized DDI management while addressing data privacy concerns.

Conclusion:

AI and ML are revolutionizing DDI detection and management, offering predictive, real-time, and personalized solutions for clinical practice. However, addressing regulatory, ethical, and data-related challenges is crucial to maximize AI’s impact on medication safety. Future research should focus on interpretable AI models, global AI standardization, and human-AI collaboration to ensure safe and effective integration into clinical workflows.