Department of Radiology, Université de Montréal, Montréal, Québec, Canada; Centre de recherche du Centre hospitalier de l'Université de Montréal, Montréal, Québec, Canada.
Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada; School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
Can Assoc Radiol J. 2018 May;69(2):120-135. doi: 10.1016/j.carj.2018.02.002. Epub 2018 Apr 11.
Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.
人工智能（AI）正在许多领域，包括医学领域，迅速从实验阶段进入实施阶段。由于可获得的大型数据集的可用性提高、计算能力的提高以及学习算法的进步，在 AI 应用程序的开发方面取得了重大的性能突破。在过去的 5 年中，被称为深度学习的 AI 技术在图像识别、标题生成和语音识别方面的性能迅速提高。放射学，特别是一个早期采用这些技术的主要候选领域。预计在未来十年中，AI 在放射学中的应用将极大地提高放射学对患者护理和人群健康的贡献的质量、价值和深度，并彻底改变放射科医生的工作流程。加拿大放射学会（CAR）是放射学的国家声音，致力于促进以患者为中心的成像、终身学习和研究的最高标准。CAR 成立了一个 AI 工作组，其任务是讨论和审议与成像中 AI 的引入和实施相关的实践、政策和患者护理问题。本白皮书根据 AI 工作组成员的审议，为 CAR 提供了建议。本白皮书介绍了放射学中的人工智能，将向 CAR 成员和政策制定者介绍有关关键术语、成员的教育需求、研究与开发、合作、潜在的临床应用、实施、结构和治理、放射科医生的作用以及 AI 对加拿大放射学的潜在影响的信息。
