Department of Radiology, University of Iowa, Iowa City, USA.
Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil.
Br J Radiol. 2023 Oct;96(1150):20221031. doi: 10.1259/bjr.20221031. Epub 2023 Apr 26.
The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI's adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists' liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.
医学影像学的快速发展给放射科医生带来了越来越大的压力。在这种情况下，人工智能（AI）成为了一个极具吸引力的合作伙伴，它可以辅助病例解读，还可以辅助放射科临床工作的其他非解读环节。在本次综述中，我们讨论了 AI 在临床实践中的解读和非解读应用，并报告了 AI 在临床应用中所面临的障碍。我们发现，AI 目前在临床实践中的应用程度中等偏低，许多放射科医生仍然对其价值和投资回报持怀疑态度。此外，我们还讨论了放射科医生对 AI 决策的责任，并解释了目前我们缺乏监管来指导可解释 AI 或自学习算法的实施。
