Public articles linked to the same research event.
PloS one This systematic review searched PubMed, Scopus, ScienceDirect, and IEEE Xplore following PRISMA 2020 and the SPIDER framework, included 10 primary studies of computational and clinical decision support interventions aimed at reducing drug-drug interactions or inappropriate prescribing, synthesized them narratively across deterministic, ontological, and stochastic/generative categories, and assessed risk of bias with PROBAST+AI, finding that earlier deterministic systems showed modest improvements in prescribing process measures with inconsistent links to patient-level outcomes, that recent stochastic and generative models reported strong internal performance metrics, and that AI-driven studies carried a consistently high risk of bias in the analysis domain driven mainly by limited external
This systematic review searched PubMed, Scopus, ScienceDirect, and IEEE Xplore following PRISMA 2020 and the SPIDER framework, included 10 primary studies of computational and clinical decision support interventions aimed at reducing drug-drug interactions or inappropriate prescribing, synthesized them narratively across deterministic, ontological, and stochastic/generative categories, and assessed risk of bias with PROBAST+AI, finding that earlier deterministic systems showed modest improvements in prescribing process measures with inconsistent links to patient-level outcomes, that recent stochastic and generative models reported strong internal performance metrics, and that AI-driven studies carried a consistently high risk of bias in the analysis domain driven mainly by limited external
This systematic review searched PubMed, Scopus, ScienceDirect, and IEEE Xplore following PRISMA 2020 and the SPIDER framework, included 10 primary studies of computational and clinical decision support interventions aimed at reducing drug-drug interactions or inappropriate prescribing, synthesized them narratively across deterministic, ontological, and stochastic/generative categories, and assessed risk of bias with PROBAST+AI, finding that earlier deterministic systems showed modest improvements in prescribing process measures with inconsistent links to patient-level outcomes, that recent stochastic and generative models reported strong internal performance metrics, and that AI-driven studies carried a consistently high risk of bias in the analysis domain driven mainly by limited external
This systematic review searched PubMed, Scopus, ScienceDirect, and IEEE Xplore following PRISMA 2020 and the SPIDER framework, included 10 primary studies of computational and clinical decision support interventions aimed at reducing drug-drug interactions or inappropriate prescribing, synthesized them narratively across deterministic, ontological, and stochastic/generative categories, and assessed risk of bias with PROBAST+AI, finding that earlier deterministic systems showed modest improvements in prescribing process measures with inconsistent links to patient-level outcomes, that recent stochastic and generative models reported strong internal performance metrics, and that AI-driven studies carried a consistently high risk of bias in the analysis domain driven mainly by limited external