Department of Radiology, University of Cambridge, CB2 0QQ Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, CB2 0RE Cambridge, United Kingdom.
Department of Engineering, University of Palermo, 90128 Palermo, Italy.
J Biomed Inform. 2020 Aug;108:103479. doi: 10.1016/j.jbi.2020.103479. Epub 2020 Jun 17.
The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine, as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms, can permit the delivery of care that outperforms what either can do separately. Therefore, Human-Computer Interaction (HCI) plays a crucial role in the design of software oriented to decision-making in medicine. In this work, we systematically review and discuss several research fields strictly linked to HCI and clinical decision-making, by subdividing the articles into six themes, namely: Interfaces, Visualization, Electronic Health Records, Devices, Usability, and Clinical Decision Support Systems. However, these articles typically present overlaps among the themes, revealing that HCI inter-connects multiple topics. With the goal of focusing on HCI and design aspects, the articles under consideration were grouped into four clusters. The advances in AI can effectively support the physicians' cognitive processes, which certainly play a central role in decision-making tasks because the human mental behavior cannot be completely emulated and captured; the human mind might solve a complex problem even without a statistically significant amount of data by relying upon domain knowledge. For this reason, technology must focus on interactive solutions for supporting the physicians effectively in their daily activities, by exploiting their unique knowledge and evidence-based reasoning, as well as improving the various aspects highlighted in this review.
不断增加的生物医学数据量正在支持新的大规模研究，尽管需要特定的计算解决方案。最新的机器学习 (ML) 和人工智能 (AI) 技术在临床研究中取得了出色的表现和重要的影响，旨在实现精准医疗，并改善医疗保健工作流程。然而，医疗保健信息源中的固有异质性和不确定性给临床医生的决策任务带来了新的挑战。只有通过明确考虑有效的和安全的交互范式，将人工智能和人类智能能力适当结合起来，才能提供超越任何一方单独作用的护理服务。因此，人机交互 (HCI) 在面向医疗决策的软件设计中起着至关重要的作用。在这项工作中，我们通过将文章细分为六个主题，即：界面、可视化、电子健康记录、设备、可用性和临床决策支持系统，系统地回顾和讨论了与 HCI 和临床决策密切相关的几个研究领域。然而，这些文章通常在主题之间存在重叠，这表明 HCI 连接了多个主题。为了关注 HCI 和设计方面，考虑的文章被分为四个集群。人工智能的进步可以有效地支持医生的认知过程，这在决策任务中肯定起着核心作用，因为人类的心理行为不能被完全模拟和捕捉；人类的思维可以通过依赖领域知识，即使没有大量的统计数据，也可以解决复杂的问题。因此，技术必须专注于交互解决方案，通过利用医生独特的知识和基于证据的推理，有效地支持他们的日常活动，并改善本综述中强调的各个方面。
