Clinician interaction with artificial intelligence systems: a narrative review

Abstract

Background and Objective: Artificial intelligence (AI) has potential to significantly enhance healthcare systems by improving diagnostic accuracy, treatment efficacy, and operational efficiency. Despite these advantages, AI adoption in clinical practice remains limited, primarily due to challenges with data integration, workflow disruptions, and distrust of AI recommendations. This study aims to perform a narrative review of the existing literature on clinician-AI interactions, focusing on how AI affects clinician experience and identify gaps in the current research for future investigations. Methods: We conducted a narrative review using a comprehensive literature search across Medline, PsychINFO, Embase, and Scopus databases for studies published from inception time of respective databases to June 2022. Keywords related to clinician-AI interactions were used. Relevant data were extracted and thematically analyzed, focusing on the elements that play key roles in the interaction between clinicians and AI systems. Key Content and Findings: We identified six thematic groups involving clinician interaction with AI systems. They were: (I) user satisfaction, learnability, and usability, demonstrating varied levels of clinician satisfaction and challenges in AI system usability; (II) accuracy of AI system outputs, with many studies validating high accuracy but noting occasional discrepancies; (III) perceived usefulness, showing that clinicians acknowledge potential benefits of AI but remain cautious; (IV) impact on clinician workflow, highlighting both improvements in efficiency and occasional workflow disruptions; (V) trust and acceptance, with trust in AI systems varying significantly among clinicians; and (VI) ethical and professional concerns, focusing on issues such as data privacy, bias, and the potential for AI to affect clinical judgment. Conclusions: This review elucidates the multifaceted nature of clinician-AI interactions, underscoring AI’s potential to enhance clinician experiences through improved diagnostic accuracy and workflow efficiency. However, it also highlights significant barriers, including ethical concerns, trust issues, and challenges in system learnability. Further research is needed to address these barriers, focusing on developing transparent AI systems, ensuring robust data privacy, and improving AI usability and integration into clinical workflows.

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