Advances, Risks, And Implementation Challenges of Artificial Intelligence as A Force Multiplier for Clinical Decision Making and Health System Efficiency

Abstract

Artificial intelligence has moved from a research promise to a clinical reality, functioning less as a replacement for practitioners than as a force multiplier that extends the reach, speed, and consistency of expert judgment across the care continuum. This review synthesizes the state of the field as of 2025, organizing the evidence around three connected questions: what artificial intelligence now does well in clinical decision making and operations, where its risks concentrate, and why implementation remains the binding constraint on realized value. It traces the conceptual evolution of the field from rule-based expert systems through statistical machine learning and deep learning to the foundation models and large language models that now dominate attention. Diagnostic and predictive applications have matured fastest, with imaging triage, early-warning models for deterioration and sepsis, and medication safety tools demonstrating strong discriminative performance and, in a growing number of cases, prospective and randomized evidence. Efficiency applications, most visibly ambient documentation systems, have produced some of the most consistent real-world gains, reducing documentation time and clinician burnout at scale. Against these advances sit persistent risks: algorithmic bias that can widen rather than narrow disparities, brittleness under distribution shift, automation bias and alert fatigue at the human interface, opacity that complicates accountability, unresolved questions of medicolegal liability, and privacy and cybersecurity exposure. Reporting and evaluation standards specific to artificial intelligence have emerged to raise the evidentiary bar, and the regulatory environment has responded with lifecycle-oriented frameworks, predetermined change control plans, and transparency expectations, yet a durable gap remains between marketing authorization and demonstrated clinical benefit. The central argument is that the value of clinical artificial intelligence is determined less by model accuracy in isolation than by the quality of the sociotechnical system into which a model is placed: governance, workflow integration, monitoring, and the preservation of meaningful human oversight. The review closes with a practical agenda for closing the gap between capability and dependable, equitable benefit.