Kitamura Felipe C, Prevedello Luciano M, Colak Errol, Halabi Safwan S, Lungren Matthew P, Ball Robyn L, Kalpathy-Cramer Jayashree, Kahn Charles E, Richards Tyler, Talbott Jason F, Shih George, Lin Hui Ming, Andriole Katherine P, Vazirabad Maryam, Erickson Bradley J, Flanders Adam E, Mongan John
From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.).
Radiol Artif Intell. 2024 May;6(3):e230227. doi: 10.1148/ryai.230227.
The Radiological Society of North America (RSNA) has held artificial intelligence competitions to tackle real-world medical imaging problems at least annually since 2017. This article examines the challenges and processes involved in organizing these competitions, with a specific emphasis on the creation and curation of high-quality datasets. The collection of diverse and representative medical imaging data involves dealing with issues of patient privacy and data security. Furthermore, ensuring quality and consistency in data, which includes expert labeling and accounting for various patient and imaging characteristics, necessitates substantial planning and resources. Overcoming these obstacles requires meticulous project management and adherence to strict timelines. The article also highlights the potential of crowdsourced annotation to progress medical imaging research. Through the RSNA competitions, an effective global engagement has been realized, resulting in innovative solutions to complex medical imaging problems, thus potentially transforming health care by enhancing diagnostic accuracy and patient outcomes. Use of AI in Education, Artificial Intelligence © RSNA, 2024.
自2017年以来，北美放射学会（RSNA）至少每年都会举办人工智能竞赛，以解决现实世界中的医学成像问题。本文探讨了组织这些竞赛所涉及的挑战和流程，特别强调了高质量数据集的创建和管理。收集多样且具有代表性的医学成像数据涉及处理患者隐私和数据安全问题。此外，确保数据的质量和一致性，包括专家标注以及考虑各种患者和成像特征，需要大量的规划和资源。克服这些障碍需要精心的项目管理并严格遵守时间表。文章还强调了众包标注在推进医学成像研究方面的潜力。通过RSNA竞赛，实现了有效的全球参与，产生了针对复杂医学成像问题的创新解决方案，从而有可能通过提高诊断准确性和改善患者治疗效果来改变医疗保健。人工智能在教育中的应用，人工智能©RSNA，2024年。
