Effectiveness of Artificial Intelligence Systems in Healthcare: A Systematic Review of Clinical and Organizational Outcomes

Background: The integration of artificial intelligence (AI)-based systems into healthcare is accelerating rapidly, yet evidence of their real-world clinical and organizational effectiveness remains fragmented. Methods: A PRISMA 2020-compliant systematic review of randomized controlled trials (RCTs) evaluating AI-based interventions in real-world healthcare settings was conducted. MEDLINE (PubMed), CINAHL, Cochrane CENTRAL, and ClinicalTrials.gov were searched up to January 2026. AI interventions were classified post hoc into three functional categories: perceptive AI, predictive/decision support AI, and conversational/adaptive AI. The direction of effect was classified as positive, neutral, mixed, or negative based on the primary endpoint, effect magnitude, and authors’ interpretation. Risk of bias was assessed using the Cochrane RoB-2 tool. Results: A total of 54 RCTs were included. Overall, 40 studies (74.1%) reported a positive direction of effect, nine a neutral result, four a mixed result, and one a negative result. Perceptive AI (n = 17) showed consistent benefits in diagnostic accuracy. Predictive/decision support AI (n = 15) showed heterogeneous results, with effectiveness dependent on workflow integration. Conversational/adaptive AI (n = 22) showed the most consistent positive signal, particularly for chronic disease self-management, behavioral change, and therapeutic adherence. Conclusions: Evidence from randomized trials suggests that AI-based interventions may provide meaningful benefits in real-world healthcare settings, with effectiveness varying by functional category, implementation context, and outcome domain. Future research should prioritize pragmatic multicenter trials with standardized reporting of clinical, organizational, and implementation outcomes.