Abstract: The increasing complexity of polypharmacy presents a significant challenge to patient safety, with adverse drug reactions (ADRs) stemming from drug-drug interactions (DDIs) representing a major cause of morbidity and mortality. Traditional DDI checking systems, which often rely on static databases, lack the contextual nuance required for effective clinical decision-making. This paper introduces Bigot-DI, an intelligent, networked application designed to predict and explain DDIs using a state-of-the-art, two-engine AI architecture. The system leverages a fine-tuned BioBERT model for high-accuracy DDI classification and a generative BioGPT model to produce real-time, audience-specific clinical summaries for both healthcare professionals and patients. By analyzing a drug pair, the system can predict the interaction type and generate a detailed report on its potential effects and mechanisms, transforming a simple query into an actionable clinical insight. This paper provides a complete blueprint for the development and deployment of this serverless application, from the fine-tuning of its biomedical language models to the design of its scalable backend API and modern frontend interface. Future work will focus on integrating diverse data sources, such as real-world evidence from the TWOSIDES dataset, to further enhance predictive accuracy and enrich the clinical reports.

Keywords: Biomedical NLP, Drug-Drug Interaction (DDI), Generative AI, Explainable AI, Clinical Decision Support, BioBERT, BioGPT, Health AI, Bioinformatics.

Downloads: | DOI: 10.17148/IJIREEICE.2025.131026