From the Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC (M.G.L.); Divisions of Radiology and Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology, Department of Pathology & Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, Ind (S.B.); Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department of Radiological Sciences, University of California Irvine, Irvine, Calif (P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI, Diagnósticos da América SA (DasaInova), São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass (M.P.L.); Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California San Francisco, San Francisco, Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology, University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA 19104-6243 (C.E.K.).
Radiol Artif Intell. 2024 Jul;6(4):e240225. doi: 10.1148/ryai.240225.
The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval. This article presents the perspectives of experts from MICCAI and RSNA on the clinical, cultural, computational, and regulatory considerations-coupled with recommended reading materials-essential to adopt AI technology successfully in radiology and, more generally, in clinical practice. The report emphasizes the importance of collaboration to improve clinical deployment, highlights the need to integrate clinical and medical imaging data, and introduces strategies to ensure smooth and incentivized integration. Adults and Pediatrics, Computer Applications-General (Informatics), Diagnosis, Prognosis © RSNA, 2024.
北美放射学会 (RSNA) 和医学影像计算与计算机辅助干预 (MICCAI) 学会牵头开展了一系列联合小组和研讨会，重点探讨了人工智能 (AI) 在放射学领域的当前影响和未来方向。这些讨论从放射学、医学成像和机器学习等多学科专家那里收集了关于 AI 技术在放射学中的当前临床应用情况以及信任、可重复性、可解释性和问责制对其的影响的观点。这些观点（既有实际的，也有哲学的）定义了共同合作的放射科医生和 AI 科学家的文化变革，并描述了 AI 技术要获得广泛认可所面临的挑战。本文介绍了来自 MICCAI 和 RSNA 的专家对临床、文化、计算和监管方面的观点——并附有推荐阅读材料——这些内容对于在放射学中成功采用 AI 技术以及更普遍地在临床实践中采用 AI 技术至关重要。该报告强调了协作对于改善临床部署的重要性，强调了需要整合临床和医学成像数据，并介绍了确保顺利和激励性整合的策略。 成人和儿科，计算机应用一般（信息学），诊断，预后 © RSNA，2024。
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