Hsiao Sabrina K, Treat Rachel M, Javan Ramin
Department of Radiology, George Washington University School of Medicine and Health Sciences, Washington, USA.
Cureus. 2024 Jul 13;16(7):e64475. doi: 10.7759/cureus.64475. eCollection 2024 Jul.
Traditional artificial intelligence (AI) tools have already been implemented in clinical radiology for lesion detection and decision-making. Generative AI (GenAI), comparingly, is a new subset of machine learning that functions based on data probabilities to create content, offering numerous capabilities yet also uncertainties. Multidisciplinary collaboration is essential in safely harnessing the power of GenAI as it transforms medicine. This paper proposes creating a GenAI task force among radiological societies, including the American College of Radiology (ACR), Society of Imaging Informatics in Medicine (SIIM), Radiological Society of North America (RSNA), European Society of Radiology (ESR), Association of University Radiologists (AUR), and American Roentgen Ray Society (ARRS) for its integration into clinical care, health policy, and education. In this paper, we explore how a task force with guidelines will help radiologists and trainees develop essential strategies for integrating evolving AI-related technologies into clinical practice.
传统人工智能（AI）工具已在临床放射学中用于病变检测和决策。相比之下，生成式人工智能（GenAI）是机器学习的一个新分支，它基于数据概率来创建内容，具有众多功能，但也存在不确定性。随着GenAI改变医学，多学科合作对于安全利用其力量至关重要。本文提议在放射学会之间成立一个GenAI特别工作组，其中包括美国放射学会（ACR）、医学影像信息学会（SIIM）、北美放射学会（RSNA）、欧洲放射学会（ESR）、大学放射科医生协会（AUR）和美国伦琴射线学会（ARRS），以便将其整合到临床护理、卫生政策和教育中。在本文中，我们探讨了一个有指导方针的特别工作组将如何帮助放射科医生和实习生制定基本策略，以便将不断发展的人工智能相关技术整合到临床实践中。
