Achieving large-scale clinician adoption of AI-enabled decision support.

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES BMJ Health & Care Informatics Pub Date : 2024-05-30 DOI:10.1136/bmjhci-2023-100971
Ian A Scott, Anton van der Vegt, Paul Lane, Steven McPhail, Farah Magrabi
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Abstract

Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making at the point of care. Statistical models developed using machine learning (ML) underpin most current tools. However, despite thousands of models and hundreds of regulator-approved tools internationally, large-scale uptake into routine clinical practice has proved elusive. While underdeveloped system readiness and investment in AI/ML within Australia and perhaps other countries are impediments, clinician ambivalence towards adopting these tools at scale could be a major inhibitor. We propose a set of principles and several strategic enablers for obtaining broad clinician acceptance of AI/ML-enabled CDS tools.

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实现临床医生大规模采用人工智能决策支持。
由人工智能(AI)支持的计算机化决策支持(CDS)工具旨在提高临床医生在医疗点决策的准确性和效率。使用机器学习(ML)开发的统计模型是目前大多数工具的基础。然而,尽管国际上有数以千计的模型和数百种监管机构批准的工具,但将其大规模应用到常规临床实践中却难以实现。虽然澳大利亚(或许还有其他国家)在人工智能/ML 方面的系统准备和投资不足是一个障碍,但临床医生对大规模采用这些工具的矛盾心理可能是一个主要抑制因素。我们提出了一套原则和若干战略推动因素,以获得临床医生对人工智能/移动终端支持的 CDS 工具的广泛接受。
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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
期刊最新文献
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