Clinical Implementation of Pharmacogenetics-Based Machine Learning Clinical Decision Support Systems: A Scoping...

BACKGROUND: Pharmacogenetics is increasingly recognized as essential for optimizing drug therapy and reducing preventable adverse drug events. However, applying pharmacogenetic data in real-world clinical decisions remains challenging. Traditional rule-based clinical decision support systems often fall short. Machine learning-based clinical decision support systems offer a more dynamic solution, combining pharmacogenetic variants, patient-specific clinical data, medication history, and guidelines to generate personalized treatment recommendations.

OBJECTIVE: This scoping review examined the extent and nature of evidence on integrating machine learning into clinical decision support systems for pharmacogenetics, emphasizing clinical implementation. By focusing on articles where tools have been integrated into clinical workflows, this review highlights progress and persistent gaps in translating pharmacogenetics-informed machine learning models into everyday clinical practice.

METHODS: A comprehensive search across multiple databases identified studies published from January 2015 to September 2025. Eligible studies included any design reporting on machine learning-based clinical decision support systems incorporating pharmacogenetic data to support therapeutic decisions in clinical settings.

RESULTS: Of 1262 records screened, 7 studies met inclusion criteria. These studies implemented machine learning-based clinical decision support systems integrating pharmacogenetic data to guide drug therapy in clinical environments. While varying in design, setting, and implementation maturity, most systems demonstrated potential benefits, such as reducing preventable adverse drug events, improving prescribing accuracy, or enhancing workflow integration. Two of the included studies evaluated PGx ML-CDSS tools in live clinical or trial workflows.

CONCLUSION: Despite growing interest and model development, clinical implementation of machine learning-based pharmacogenetic clinical decision support tools remains limited. Most systems remain rule-based, while a few integrate machine learning with pharmacogenetic data for more personalized and adaptive decision-making. This review underscores the gap between theoretical model development and real-world application. To advance the field, implementation science research is needed to evaluate usability, clinical workflow integration, and patient outcomes. Real-world evidence is essential to unlock the full potential of machine learning-based clinical decision support in pharmacogenetics.