Objectives: Artificial intelligence (AI) is a transformative development in the field of medicine. In the field of musculoskeletal radiology, agentic AI is a technology that could flourish, but currently, the limited evidence base is fragmented and sparse, and we present a scoping review of it. Methods: Parallel searches were conducted in four databases: PubMed, Embase, Scopus, and Web of Science. Search terms included all agentic AI and autonomous AI agents, as well as radiology. All papers underwent screening by two independent reviewers, with conflicts resolved through consensus. Initially, inclusion criteria involved all papers on general radiology, which were later stratified for musculoskeletal radiology and applicable papers to ensure inclusion of all suitable studies. A thematic analysis was undertaken by two independent reviewers. Results: Eleven studies met the inclusion criteria, comprising two MSK (musculoskeletal)-specific and nine general radiology papers applicable to MSK workflows. Four key themes emerged. Agentic decision support was demonstrated across five studies, showing improved diagnostic coordination, pathway navigation, and reduced clinician workload. Workflow optimisation was highlighted in four studies, with agentic systems enhancing administrative efficiency, modality selection, and overall radiology throughput. Image analysis and reconstruction were improved in three studies, with multi-agent systems enabling enhanced image quality and automated interpretation. Finally, four studies addressed conceptual, ethical, and governance considerations, emphasising the need for transparency, safety frameworks, and clinician oversight. Conclusion: Agentic AI shows considerable promise for enhancing MSK radiology through improved decision support, image analysis, and workflow efficiency; however, the current evidence remains limited and largely theoretical.
Gibson et al. (Sun,) studied this question.