Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care. In response to the increased use of MI in healthcare, and issues associated when applying such approaches to clinical care settings, the National Institutes of Health (NIH) and National Center for Advancing Translational Sciences (NCATS) co-hosted a Machine Intelligence in Healthcare workshop with the National Cancer Institute (NCI) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) on 12 July 2019. Speakers and attendees included researchers, clinicians and patients/ patient advocates, with representation from industry, academia, and federal agencies. A number of issues were addressed, including: data quality and quantity; access and use of electronic health records (EHRs); transparency and explainability of the system in contrast to the entire clinical workflow; and the impact of bias on system outputs, among other topics. This whitepaper reports on key issues associated with MI specific to applications in the healthcare field, identifies areas of improvement for MI systems in the context of healthcare, and proposes avenues and solutions for these issues, with the aim of surfacing key areas that, if appropriately addressed, could accelerate progress in the field effectively, transparently, and ethically.

中文翻译：

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医疗保健中的机器智能——可信度、可解释性、可用性和透明度的视角

机器智能 (MI) 正在迅速成为生物医学发现、临床研究、医疗诊断/设备和精准医学的重要方法。这些工具可以为研究人员、医生和患者揭示新的可能性，使他们能够做出更明智的决策并取得更好的结果。当在医疗保健环境中部署时，这些方法有可能提高健康研究和护理生态系统的效率和有效性，并最终提高患者护理的质量。为了应对医疗保健中 MI 的使用增加，以及将此类方法应用于临床护理环境时出现的相关问题，美国国立卫生研究院 (NIH) 和国家转化科学促进中心 (NCATS) 共同主办了医疗保健中的机器智能研讨会于 2019 年 7 月 12 日与国家癌症研究所 (NCI) 和国家生物医学成像和生物工程研究所 (NIBIB) 共同举办。演讲者和与会者包括研究人员、临床医生和患者/患者权益倡导者，以及来自行业、学术界和联邦机构的代表。解决了许多问题，包括：数据质量和数量；访问和使用电子健康记录 (EHR)；与整个临床工作流程相比，系统的透明度和可解释性；以及偏差对系统输出的影响等主题。本白皮书报告了与医疗保健领域特定应用的 MI 相关的关键问题，确定了医疗保健背景下 MI 系统的改进领域，并提出了这些问题的途径和解决方案，目的是在适当的情况下提出关键领域得到解决，可以有效、透明和合乎道德地加速该领域的进展。