2025/07/24 by Nicholas Dietrich · 1 citation
Engineering · Medicine · #Artificial Intelligence in Healthcare and Education #Medical Imaging and Analysis #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.1093/bjr/tqaf173
crossref issued 2025/07/24 · crossref published 2025/07/24 · crossref published-online 2025/07/24 · openalex publication_date 2025/07/24 · crossref created 2025/07/24 · crossref published-print 2025/10/01 · openalex created_date 2025/10/10 · crossref deposited 2025/10/11 · openalex updated_date 2026/07/31 · crossref indexed 2026/07/31
This commentary introduces agentic artificial intelligence (AI) as an emerging paradigm in radiology, marking a shift from passive, user-triggered tools to systems capable of autonomous workflow management, task planning, and clinical decision support. Agentic AI models may dynamically prioritize imaging studies, tailor recommendations based on patient history and scan context, and automate administrative follow-up tasks, offering potential gains in efficiency, triage accuracy, and cognitive support. While not yet widely implemented, early pilot studies and proof-of-concept applications highlight promising utility across high-volume and high-acuity settings. Key barriers, including limited clinical validation, evolving regulatory frameworks, and integration challenges, must be addressed to ensure safe, scalable deployment. Agentic AI represents a forward-looking evolution in radiology that warrants careful development and clinician-guided implementation.