Applications of artificial intelligence in clinical management, research, and health administration: imaging perspectives with a focus on hemophilia

Joints of persons with hemophilia are frequently affected by repetitive hemarthrosis. In this paper, concepts, perks, and quirks of the use of artificial intelligence (AI), machine learning (ML), and deep learning are reviewed within clinical and research contexts of hemophilia and other blood-induced disorders’ patient care, targeted to the imaging diagnosis of hemophilic joints, under the perspective of different stakeholders (radiologists, hematologists, nurses, physiotherapists, technologists, researchers, managers, and patients/caregivers). Rubrics that determine the suitability of the utilization of AI in blood-induced disorders’ patient care, including diagnosis and follow-up of patients are discussed, focusing on features in which AI can replace or augment the role of radiology in the clinical management and in research of patients. Insights on features in the design and conduct of AI projects in which the human intervention remains critical are provided. The author discusses research concepts in radiogenomics, and challenges for the utilization of AI in different healthcare fields such as patient safety, data sharing and privacy regulations, workforce education and future jobs’ shortage. Finally, the author proposes alternatives and potential solutions to mitigate challenges in successfully deploying ML algorithms into clinical practice.

血友病患者的关节常受复发性关节积血困扰。本文围绕血友病及其他血液源性疾病患者的临床与研究照护场景，针对血友病性关节的影像诊断需求，从不同利益相关方（放射科医师、血液科医师、护士、物理治疗师、医技人员、科研人员、管理者以及患者/照护者）的视角，综述了人工智能（AI）、机器学习（ML）与深度学习的应用理念、优势与特有局限。本文探讨了用于判定AI在血液源性疾病患者照护（包括患者诊断与随访）中应用适宜性的评估标准，重点聚焦于AI可替代或增强放射科在患者临床管理与科研中作用的相关场景。本文还针对AI项目设计与实施环节中仍需关键人工干预的核心要点提供了相关洞见。作者探讨了放射基因组学相关的研究理念，以及AI在不同医疗领域应用所面临的挑战，涵盖患者安全、数据共享与隐私监管、从业人员教育以及未来岗位短缺等方面。最后，作者针对将ML算法成功部署至临床实践过程中面临的各类挑战，提出了替代方案与潜在解决方案。