Background: Clinical decision support systems are integral components of modern healthcare information technology, aiming to enhance medication safety by identifying potential drug–drug interactions (DDIs). Traditional expert-curated databases, such as LexiDrug, provide structured interaction data but may vary in coverage and clinical recommendations. Meanwhile, retrieval-augmented generative artificial intelligence tools such as ClinicalKey AI offer narrative-based DDI assessments with contextual explanations, yet their real-world performance and clinical relevance remain underexplored within electronic clinical workflows. Objective: To evaluate and compare the performance of a retrieval-augmented generative AI system (ClinicalKey AI) against a human-curated expert system (UpToDate LexiDrug) in the screening and characterization of clinically important DDIs, with emphasis on implications for clinical decision support and healthcare delivery. Methods: A reference set of 280 clinically relevant drug pairs was compiled. For each pair, data were extracted from LexiDrug, including interaction mechanism, severity category, recommended management actions, and evidence levels. CKAI was queried with standardized prompts to obtain equivalent outputs. Agreement between systems was measured for mechanism classification and severity ratings. Differences in management recommendations and supplemental information (e.g., onset, risk factors, alternative suggestions) were analyzed descriptively. The evaluation focused on clinical decision support relevance, information richness, and potential impacts on healthcare practice. Results: CKAI and the expert system agreed on interaction mechanism classification in over 99% of cases, supporting the AI’s ability to recognize basic pharmacological relationships. However, CKAI consistently assigned higher severity levels compared to LexiDrug and frequently recommended “avoid” actions rather than nuanced modifications. CKAI provided significantly more contextual information on interaction onset, patient risk factors, and alternative therapy options, enhancing interpretability for clinical users. Divergences in evidence categorization highlighted variability in underlying knowledge representation between generative AI and curated sources. Conclusion: Retrieval-augmented generative AI demonstrates high concordance with expert systems in identifying DDI mechanisms and yields rich contextual insights that may support clinician interpretation.