负责任的人工智能实践和人工智能教育是人工智能实施的核心:对欧洲所有医学成像专业人员的快速审查

BJR open Pub Date : 2023-06-30 eCollection Date: 2023-01-01 DOI:10.1259/bjro.20230033
Gemma Walsh, Nikolaos Stogiannos, Riaan van de Venter, Clare Rainey, Winnie Tam, Sonyia McFadden, Jonathan P McNulty, Nejc Mekis, Sarah Lewis, Tracy O'Regan, Amrita Kumar, Merel Huisman, Sotirios Bisdas, Elmar Kotter, Daniel Pinto Dos Santos, Cláudia Sá Dos Reis, Peter van Ooijen, Adrian P Brady, Christina Malamateniou
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引用次数: 0

摘要

人工智能(AI)已经从实验室过渡到床边,并越来越多地用于医疗保健。放射学和放射摄影处于人工智能实施的前沿,因为大数据可以用于不同患者群体的医学成像和诊断。安全有效地实施人工智能需要所有关键利益相关者都坚持负责任和道德的做法,不同专业团体之间有和谐的合作,并为所有相关人员提供定制的教育规定。本文概述了道德和负责任的人工智能的关键原则,重点介绍了最近针对临床从业人员的教育举措,并讨论了所有医学成像专业人员在为欧洲的数字未来做准备时的协同作用。负责任和道德的人工智能对于增强医疗保健专业人员和患者的安全和信任文化至关重要。为医疗成像专业人员提供有关人工智能的教育和培训,对于理解人工智能的基本原理和应用至关重要,目前欧洲提供了许多此类服务。教育可以促进人工智能工具的透明度,但需要更正式的、由大学主导的培训,以确保坚持学术审查、适当的教学法、多学科和针对学习者独特需求的定制。随着放射技师和放射科医生与其他专业人员一起工作,了解和利用人工智能在医学成像中的好处,很明显,他们面临着同样的挑战,他们有同样的需求。数字未来属于多学科团队,这些团队可以无缝合作,共同学习,共同管理风险,并为他们所服务的患者的利益而合作。
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Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe.

Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and effective AI implementation requires that responsible and ethical practices are upheld by all key stakeholders, that there is harmonious collaboration between different professional groups, and customised educational provisions for all involved. This paper outlines key principles of ethical and responsible AI, highlights recent educational initiatives for clinical practitioners and discusses the synergies between all medical imaging professionals as they prepare for the digital future in Europe. Responsible and ethical AI is vital to enhance a culture of safety and trust for healthcare professionals and patients alike. Educational and training provisions for medical imaging professionals on AI is central to the understanding of basic AI principles and applications and there are many offerings currently in Europe. Education can facilitate the transparency of AI tools, but more formalised, university-led training is needed to ensure the academic scrutiny, appropriate pedagogy, multidisciplinarity and customisation to the learners' unique needs are being adhered to. As radiographers and radiologists work together and with other professionals to understand and harness the benefits of AI in medical imaging, it becomes clear that they are faced with the same challenges and that they have the same needs. The digital future belongs to multidisciplinary teams that work seamlessly together, learn together, manage risk collectively and collaborate for the benefit of the patients they serve.

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