From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, FND-210, Boston, MA 02114-2698 (O.S.P., J.A.B.); International Society for Strategic Studies in Radiology (IS3R), Vienna, Austria (M.D., D.R.E., C.J.H., S.O.S., J.A.B.); Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (G.L., C.J.H.); Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Boston, Mass (G.L.); Department of Radiology, Charité-Universitätsmedizin, Berlin, Germany (M.D.); Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Calif (D.R.E.); and Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany (S.O.S.).
Radiology. 2020 Oct;297(1):6-14. doi: 10.1148/radiol.2020200038. Epub 2020 Aug 25.
Artificial intelligence (AI) is becoming increasingly present in radiology and health care. This expansion is driven by the principal AI strengths: automation, accuracy, and objectivity. However, as radiology AI matures to become fully integrated into the daily radiology routine, it needs to go beyond replicating static models, toward discovering new knowledge from the data and environments around it. Continuous learning AI presents the next substantial step in this direction and brings a new set of opportunities and challenges. Herein, the authors discuss the main concepts and requirements for implementing continuous AI in radiology and illustrate them with examples from emerging applications.
人工智能（AI）在放射学和医疗保健领域的应用日益广泛。这一扩张主要得益于 AI 的三大优势：自动化、准确性和客观性。然而，随着放射学 AI 逐渐成熟并完全融入日常放射学常规，它需要超越复制静态模型，转而从其周围的数据和环境中发现新知识。持续学习 AI 是朝着这个方向迈出的下一个重要步骤，它带来了一系列新的机遇和挑战。在此，作者讨论了在放射学中实施持续 AI 的主要概念和要求，并通过新兴应用示例加以说明。
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