Review of agentic artificial intelligence (AI) in radiology: from current clinical integration to future innovations.
Authors
Affiliations (5)
Affiliations (5)
- Emergency and Trauma Radiology, Sunnybrook Health Sciences Centre, University of Toronto, Canada.
- University of Birmingham, College of Medicine and Dental Sciences, Birmingham, UK.
- Department of Radiology, AIG Hospitals, Hyderabad, India.
- Department of Diagnostic and Interventional Radiology, AIIMS, Rishikesh, India.
- Department of Musculoskeletal Radiology, Royal Orthopaedic Hospital, Birmingham, UK. Electronic address: [email protected].
Abstract
Artificial intelligence (AI) is one of the most revolutionary developments in the field of medicine in recent history, with radiology being one of the strongest beneficiaries. AI models predominantly relied on user input to generate 'human-like' responses through a series of algorithms. Newer developments in this domain include agentic AI systems, where individual AI systems work on prescribed tasks. This study reviews the current evidence base to provide a synthesis of the present literature. For this scoping review, parallel searches of PubMed, Embase, Web of Science, and Scopus were conducted for all papers on the theme of agentic AI in radiology published between January 2015 and October 2025. Papers were then screened by two independent reviewers, with conflicts resolved through consensus. Data were extracted from papers according to a predetermined data extraction table and was grouped by common themes to provide a synthesis of the current evidence base. Searches yielded a total of 129 articles, 27 of which were included in the final review after screening. There were 5 main themes identified across the 27 studies: the role of agentic AI in autonomous clinical decision support; workflow orchestration; multimodal systems for image analysis; reporting and communications; and ethical guidance. Across all included studies, many were technical papers or exploratory, highlighting the need for prospective real-world application studies to assess integration into clinical workflows. Agentic AI provides an exciting and novel way to improve workflow efficiency and streamline reporting pipelines.