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

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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.

Scott, I. A., van der Vegt, A., Lane, P., McPhail, S., Magrabi, F.

Scott, I. A., van der Vegt, A., Lane, P., McPhail, S., Magrabi, F.

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