Mapping the Global Research on Drug–Drug Interactions: A Multidecadal Evolution Through AI-Driven Terminology Standardization

The significant burden of polypharmacy in clinical settings contrasts sharply with the narrow research focus on drug–drug interactions (DDIs), revealing an important gap in understanding the complexity of real-world multi-drug regimens. The present study addresses this gap by conducting a high-resolution, multidimensional bibliometric and network analysis of 19,151 DDI publications indexed in the Web of Science Core Collection (1975–2025). Using advanced tools, including VOSviewer version 1.6.20, Bibliometrix 5.0.0, and AI-enhanced terminology normalization, global research trajectories, knowledge clusters, and collaborative dynamics were systematically mapped. The analysis revealed an exponential growth in publication volume (from 55 in 1990 to 1194 in 2024), with output led by the United States and a marked acceleration in Chinese contributions after 2015. Key pharmacological agents frequently implicated in DDI research included CYP450-dependent drugs such as statins, antiretrovirals, and central nervous system drugs. Thematic clusters evolved from mechanistic toxicity assessments to complex frameworks involving clinical risk management, oncology co-therapies, and pharmacokinetic modeling. The citation impact peaked at 3.93 per year in 2019, reflecting the increasing integration of DDI research into mainstream areas of pharmaceutical science. The findings highlight a shift toward addressing polypharmacy risks in aging populations, supported by novel computational methodologies. This comprehensive assessment offers insights for researchers and academics aiming to navigate the evolving scientific landscape of DDIs and underlines the need for more nuanced system-level approaches to interaction risk assessment. Future studies should aim to incorporate patient-level real-world data, expand bibliometric coverage to underrepresented regions and non-English literature, and integrate pharmacogenomic and time-dependent variables to enhance predictive models of interaction risk. Cross-validation of AI-based approaches against clinical outcomes and prospective cohort data are also needed to bridge the translational gap and support precision dosing in complex therapeutic regimens.