Lastrucci Andrea, Iosca Nicola, Wandael Yannick, Barra Angelo, Lepri Graziano, Forini Nevio, Ricci Renzo, Miele Vittorio, Giansanti Daniele
Department of Allied Health Professions, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy.
Unità Sanitaria Locale Umbria 1, Via Guerriero Guerra 21, 06127 Perugia, Italy.
Diagnostics (Basel). 2025 Apr 1;15(7):893. doi: 10.3390/diagnostics15070893.
The integration of artificial intelligence in interventional radiology is an emerging field with transformative potential, aiming to make a great contribution to the health domain. This overview of reviews seeks to identify prevailing themes, opportunities, challenges, and recommendations related to the process of integration. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements in, and future implications of, this domain. In total, 27 review studies were selected through the systematic process. Based on the overview, the integration of artificial intelligence (AI) in interventional radiology (IR) presents significant opportunities to enhance precision, efficiency, and personalization of procedures. AI automates tasks like catheter manipulation and needle placement, improving accuracy and reducing variability. It also integrates multiple imaging modalities, optimizing treatment planning and outcomes. AI aids intra-procedural guidance with advanced needle tracking and real-time image fusion. Robotics and automation in IR are advancing, though full autonomy in AI-guided systems has not been achieved. Despite these advancements, the integration of AI in IR is complex, involving imaging systems, robotics, and other technologies. This complexity requires a comprehensive certification and integration process. The role of regulatory bodies, scientific societies, and clinicians is essential to address these challenges. Standardized guidelines, clinician education, and careful AI assessment are necessary for safe integration. The future of AI in IR depends on developing standardized guidelines for medical devices and AI applications. Collaboration between certifying bodies, scientific societies, and legislative entities, as seen in the EU AI Act, will be crucial to tackling AI-specific challenges. Focusing on transparency, data governance, human oversight, and post-market monitoring will ensure AI integration in IR proceeds with safeguards, benefiting patient outcomes and advancing the field.
人工智能在介入放射学中的整合是一个具有变革潜力的新兴领域，旨在为健康领域做出巨大贡献。这篇综述性概述旨在确定与整合过程相关的主要主题、机遇、挑战和建议。本综述利用标准化清单和质量控制程序，审视了该领域的最新进展及其未来影响。通过系统的筛选过程，共选取了27项综述研究。基于该综述，人工智能（AI）在介入放射学（IR）中的整合为提高手术的精准性、效率和个性化提供了重大机遇。人工智能可实现导管操作和针穿刺等任务的自动化，提高准确性并减少变异性。它还能整合多种成像模式，优化治疗计划和结果。人工智能借助先进的针追踪和实时图像融合辅助术中引导。介入放射学中的机器人技术和自动化正在不断发展，不过人工智能引导系统尚未实现完全自主。尽管有这些进展，但人工智能在介入放射学中的整合很复杂，涉及成像系统、机器人技术和其他技术。这种复杂性需要一个全面的认证和整合过程。监管机构、科学协会和临床医生的作用对于应对这些挑战至关重要。标准化指南、临床医生教育以及对人工智能的谨慎评估对于安全整合是必要的。人工智能在介入放射学中的未来取决于为医疗设备和人工智能应用制定标准化指南。正如欧盟人工智能法案所示，认证机构、科学协会和立法实体之间的合作对于应对人工智能特有的挑战至关重要。关注透明度、数据治理、人工监督和上市后监测将确保人工智能在介入放射学中的整合在保障措施下进行，有利于患者治疗结果并推动该领域发展。
