Cutillo Christine M, Sharma Karlie R, Foschini Luca, Kundu Shinjini, Mackintosh Maxine, Mandl Kenneth D
1National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD USA.
2Evidation Health Inc., San Mateo, CA USA.
NPJ Digit Med. 2020 Mar 26;3:47. doi: 10.1038/s41746-020-0254-2. eCollection 2020.
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.
机器智能（MI）正迅速成为生物医学发现、临床研究、医学诊断/设备以及精准医疗等领域的重要方法。此类工具能够为研究人员、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。当应用于医疗保健环境时，这些方法有潜力提高健康研究和护理生态系统的效率和效果，并最终改善患者护理质量。为应对MI在医疗保健领域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）和国家推进转化科学中心（NCATS）于2019年7月12日与美国国家癌症研究所（NCI）和国家生物医学成像与生物工程研究所（NIBIB）共同主办了一场医疗保健领域的机器智能研讨会。演讲者和与会者包括研究人员、临床医生以及患者/患者权益倡导者，来自行业、学术界和联邦机构。会议讨论了多个问题，包括：数据质量和数量；电子健康记录（EHR）的获取和使用；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其他主题。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，并针对这些问题提出了途径和解决方案，旨在揭示关键领域，若能适当解决这些领域的问题，可有效、透明且合乎道德地加速该领域的进展。
