Dietrich Nicholas
Temerty Faculty of Medicine, University of Toronto, 1 King's College Cir, Toronto, Ontario, Canada M5S 1A8.
Institute of Biomedical Engineering, University of Toronto, 164 College St, Toronto, Ontario, Canada, M5S 3G9.
Br J Radiol. 2025 Jul 24. doi: 10.1093/bjr/tqaf173.
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.
本评论介绍了智能人工智能（AI）作为放射学中的一种新兴范式，标志着从被动的、用户触发的工具向能够自主进行工作流程管理、任务规划和临床决策支持的系统转变。智能人工智能模型可以动态地对影像检查进行优先级排序，根据患者病史和扫描背景定制建议，并自动执行行政后续任务，有望提高效率、分诊准确性和认知支持。虽然尚未广泛实施，但早期的试点研究和概念验证应用突出了其在高工作量和高急症情况下的潜在效用。必须解决包括临床验证有限、监管框架不断演变以及集成挑战在内的关键障碍，以确保安全、可扩展的部署。智能人工智能代表了放射学中一项具有前瞻性的发展，值得谨慎开发和临床医生指导下的实施。
