Factors influencing trust in medical artificial intelligence for healthcare professionals: a narrative review

Abstract

Objective: We performed a comprehensive review of the literature to better understand the trust dynamics between medical artificial intelligence (AI) and healthcare expert end-users. We explored the factors that influence trust in these technologies and how they compare to established concepts of trust in the engineering discipline. By identifying the qualitatively and quantitatively assessed factors that influence trust in medical AI, we gain insight into understanding how autonomous systems can be optimized during the development phase to improve decision-making support and clinician-machine teaming. This facilitates an enhanced understanding of the qualities that healthcare professional users seek in AI to consider it trustworthy. We also highlight key considerations for promoting on-going improvement of trust in autonomous medical systems to support the adoption of medical technologies into practice. Background: decision support systems introduces challenges and barriers to adoption and implementation into clinical practice. Methods: We searched databases including, Ovid MEDLINE, Ovid EMBASE, Clarivate Web of Science, and Google Scholar, as well as gray literature, for publications from 2000 to July 15, 2021, that reported features of AI-based diagnostic and clinical decision support systems that contribute to enhanced end-user trust. Papers discussing implications and applications of medical AI in clinical practice were also recorded. Results were based on the quantity of papers that discussed each trust concept, either quantitatively or qualitatively, using frequency of concept commentary as a proxy for importance of a respective concept. Conclusions: Explainability, transparency, interpretability, usability, and education are among the key identified factors thought to influence a healthcare professionals’ trust in medical AI and enhance clinician-machine teaming in critical decision-making healthcare environments. We also identified the need to better evaluate and incorporate other critical factors to promote trust by consulting medical professionals when developing AI systems for clinical decision-making and diagnostic support.