Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada.
Can Assoc Radiol J. 2024 Aug;75(3):558-567. doi: 10.1177/08465371241236376. Epub 2024 Mar 6.
Artificial intelligence (AI) is rapidly evolving and has transformative potential for interventional radiology (IR) clinical practice. However, formal training in AI may be limited for many clinicians and therefore presents a challenge for initial implementation and trust in AI. An understanding of the foundational concepts in AI may help familiarize the interventional radiologist with the field of AI, thus facilitating understanding and participation in the development and deployment of AI. A pragmatic classification system of AI based on the complexity of the model may guide clinicians in the assessment of AI. Finally, the current state of AI in IR and the patterns of implementation are explored (pre-procedural, intra-procedural, and post-procedural).
人工智能(AI)正在迅速发展,对介入放射学(IR)临床实践具有变革性的潜力。然而,许多临床医生可能接受的 AI 正规培训有限,因此在初始实施和对 AI 的信任方面存在挑战。了解 AI 的基础概念可能有助于介入放射科医生熟悉 AI 领域,从而促进对 AI 的开发和部署的理解和参与。基于模型复杂性的 AI 实用分类系统可以指导临床医生对 AI 进行评估。最后,探讨了 IR 中 AI 的现状和实施模式(术前、术中、术后)。