Ling Kuo Rachel Yi, Freethy Alexander, Smith Judi, Hill Rosie, C Joanna, Jerome Derek, Harriss Eli, Collins Gary S, Tutton Elizabeth, Furniss Dominic
Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford, UK.
Department of Plastic Surgery, Royal Devon and Exeter Hospital, Royal Devon University Healthcare NHS Foundation Trust, UK.
EClinicalMedicine. 2024 Mar 22;71:102555. doi: 10.1016/j.eclinm.2024.102555. eCollection 2024 May.
Diagnosis is a cornerstone of medical practice. Worldwide, there is increased demand for diagnostic services, exacerbating workforce shortages. Artificial intelligence (AI) technologies may improve diagnostic efficiency, accuracy, and access. Understanding stakeholder perspectives is key to informing implementation of complex interventions. We systematically reviewed the literature on stakeholder perspectives on diagnostic AI, including all English-language peer-reviewed primary qualitative or mixed-methods research.
We searched PubMed, Ovid MEDLINE/Embase, Scopus, CINAHL and Web of Science (22/2/2023 and updated 8/2/2024). The Critical Appraisal Skills Programme Checklist informed critical appraisal. We used a 'best-fit' framework approach for analysis, using the Non-adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework. This study was pre-registered (PROSPERO CRD42022313782).
We screened 16,577 articles and included 44. 689 participants were interviewed, and 402 participated in focus groups. Four stakeholder groups were described: patients, clinicians, researchers and healthcare leaders. We found an under-representation of patients, researchers and leaders across articles. We summarise the differences and relationships between each group in a conceptual model, hinging on the establishment of trust, engagement and collaboration. We present a modification of the NASSS framework, tailored to diagnostic AI.
We provide guidance for future research and implementation of diagnostic AI, highlighting the importance of representing all stakeholder groups. We suggest that implementation strategies consider how any proposed software fits within the extended NASSS-AI framework, and how stakeholder priorities and concerns have been addressed.
RK is supported by an NIHR Doctoral Research Fellowship grant (NIHR302562), which funded patient and public involvement activities, and access to Covidence.
诊断是医疗实践的基石。在全球范围内,对诊断服务的需求不断增加,加剧了劳动力短缺的问题。人工智能(AI)技术可能会提高诊断效率、准确性并改善可及性。了解利益相关者的观点是为复杂干预措施的实施提供信息的关键。我们系统地回顾了关于利益相关者对诊断性人工智能观点的文献,包括所有英文同行评审的原发性定性或混合方法研究。
我们检索了PubMed、Ovid MEDLINE/Embase、Scopus、CINAHL和Web of Science(2023年2月22日,并于2024年2月8日更新)。批判性评估技能计划清单为批判性评估提供了依据。我们使用“最佳拟合”框架方法进行分析,采用非采用、放弃、扩大规模、传播、可持续性(NASSS)框架。本研究已预先注册(PROSPERO CRD42022313782)。
我们筛选了16577篇文章,纳入了44篇。采访了689名参与者,402人参加了焦点小组。描述了四个利益相关者群体:患者、临床医生、研究人员和医疗保健领导者。我们发现,在各篇文章中,患者、研究人员和领导者的代表性不足。我们在一个概念模型中总结了每个群体之间的差异和关系,该模型取决于信任、参与和合作的建立。我们提出了一个针对诊断性人工智能的NASSS框架修改版。
我们为诊断性人工智能的未来研究和实施提供了指导,强调了代表所有利益相关者群体的重要性。我们建议实施策略应考虑任何提议的软件如何适用于扩展后的NASSS-AI框架,以及利益相关者的优先事项和关注点是如何得到解决的。
RK得到了NIHR博士研究奖学金(NIHR302562)的支持,该奖学金资助了患者和公众参与活动,并提供了使用Covidence的机会。