TRANSFORMING RADIOLOGY WORKFLOW WITH ARTIFICIAL INTELLIGENCE: A COMPREHENSIVE REVIEW

Abstract

Background: Artificial intelligence (AI) is increasingly being incorporated into radiology, not only for image interpretation but also for scheduling, examination protocoling, image acquisition, reconstruction, worklist prioritisation, quantitative analysis, reporting, communication, and follow-up. The clinical value of these systems depends on more than algorithmic accuracy. It also depends on interoperability, usability, external validation, human oversight, institutional readiness, and the ability to demonstrate measurable improvement in patient care. Objective: This review examines how AI is reshaping radiology workflow, summarises clinically relevant applications across imaging modalities, evaluates evidence regarding diagnostic performance and operational efficiency, and discusses implementation, ethics, regulation, workforce, economic, and equity-related considerations. Methods: A structured narrative review framework was developed using PubMed/MEDLINE, Embase, Scopus, Web of Science, Cochrane Library, PubMed Central, major radiology journals, and publicly available regulatory and professional sources. Original clinical research articles published between January 2016 and August 2026 were prioritised. Studies were considered when they evaluated an AI application in clinical imaging, measured diagnostic or workflow outcomes, or described prospective implementation. Because the studies differed substantially in task, population, modality, endpoint, and reference standard, findings were synthesised narratively rather than pooled statistically. Results: AI has demonstrated value in selected tasks involving mammographic screening, chest-radiograph interpretation, CT triage, MRI reconstruction, segmentation, quantitative imaging, and worklist prioritisation. Prospective and randomised studies suggest that AI can preserve or improve diagnostic performance while reducing selected forms of reader workload. Nevertheless, reported workflow gains are variable. Benefits may be attenuated by false-positive alerts, additional review requirements, poor system integration, case-mix differences, and local staffing patterns. Evidence connecting AI deployment with improved patient outcomes, cost-effectiveness, and long-term equity remains less mature. Conclusions: AI should be treated as a sociotechnical intervention rather than as a stand-alone software product. The most defensible implementation strategy is to begin with narrowly defined clinical problems, conduct local validation, integrate outputs into existing systems, train users, monitor performance after deployment, and retain accountable human oversight. Sustainable transformation will require prospective multicentre research, transparent reporting, interoperable architectures, lifecycle regulation, and deliberate protection against bias and unequal access.