Actionable artificial intelligence: Overcoming barriers to adoption of prediction tools

Abstract

Clinical prediction models based on artificial intelligence algorithms can potentially improve patient care, reduce errors, and add value to the health care system. However, their adoption is hindered by legitimate economic, practical, professional, and intellectual concerns. This article explores these barriers and highlights well-studied instruments that can be used to overcome them. Adopting actionable predictive models will require the purposeful incorporation of patient, clinical, technical, and administrative perspectives. Model developers must articulate a priori clinical needs, ensure explainability and low error frequency and severity, and promote safety and fairness. Models themselves require ongoing validation and monitoring to address variations in health care settings and must comply with an evolving regulatory environment. Through these principles, surgeons and health care providers can leverage artificial intelligence to optimize patient care.