Artificial Intelligence for Clinical Decision Support in Internal Medicine: A Systematic Review

Artificial intelligence (AI) models perform well on retrospective internal medicine datasets, but few have been deployed prospectively as clinician-facing decision support and evaluated for their effect on care. The purpose of this study was to synthesise original primary research evaluating prospectively deployed AI-based clinical decision support systems (CDSSs) in adult internal medicine and acute general medical care. Five databases were searched from inception to 31 May 2026 for original peer-reviewed studies in which an AI-derived CDSS was deployed in a live clinical workflow and compared with usual care or a pre-implementation period. Non-original publications and retrospective model development or validation studies were excluded. Screening, extraction and risk-of-bias assessment (RoB 2; ROBINS-I) were performed in duplicate. Heterogeneity precluded meta-analysis, so findings were synthesised structurally. Nine studies (2020-2025) were included: six randomised trials and three non-randomised implementation studies from the United States (n = 6), Taiwan (n = 2) and Thailand (n = 1), contributing 110,452 patients plus one clinician-level trial of 50 physicians. Four domains emerged: sepsis (four studies), inpatient deterioration (one), AI-enabled electrocardiography (three) and large language model-assisted diagnostic reasoning (one). Process outcomes improved consistently, including shorter time to antibiotics, higher sepsis bundle compliance, more new diagnoses of low ejection fraction and better diagnostic yield of confirmatory testing. A mortality benefit was reported in four studies, but three of these were non-randomised, and in the only randomised trial with mortality as its primary endpoint the effect was concentrated in an algorithm-defined high-risk subgroup. The single large language model trial showed no improvement in diagnostic reasoning. Three randomised trials were at low risk of bias, two raised some concerns and one was at high risk; two non-randomised studies were at moderate and one at serious risk of bias. Deployed AI-based CDSSs reliably improve internal medicine care processes, but evidence for patient-important benefit remains limited, concentrated in sepsis and cardiac phenotyping, and geographically narrow. Benefit appears to depend on how alerts are routed and acted upon as much as on model discrimination. Multi-centre randomised trials with patient-important primary endpoints are required before broad adoption.