Health Care Professionals' Experience of Using AI: Systematic Review With Narrative Synthesis

Area of Science:

• Healthcare Informatics
• Artificial Intelligence in Medicine
• Clinical Decision Support Systems

Background:

• Artificial intelligence (AI) tools are increasingly used for clinical decision support, shifting from knowledge-based to machine learning-based systems.
• Understanding healthcare professionals' trust and perceived benefits is crucial for adopting AI technologies.
• Experiences with non-knowledge-based AI clinical decision support tools remain poorly understood.

Purpose of the Study:

• To qualitatively synthesize evidence on healthcare professionals' experiences with non-knowledge-based AI tools in routine clinical decision-making.
• To explore the factors influencing the adoption and effective use of AI in healthcare settings.

Main Methods:

• A comprehensive search of four electronic databases (MEDLINE, Embase, CINAHL, Web of Science) was conducted in June 2023.
• Included studies of any design reporting healthcare professionals' experiences with non-knowledge-based AI for clinical decision support.
• Thematic synthesis was employed following double independent quality assessment using the Mixed Methods Appraisal Tool.

Main Results:

• Twenty-five studies from nine countries were included, with a majority being qualitative.
• Seven key themes emerged: understanding AI, trust and confidence, perceived value, data limitations, time constraints, governance concerns, and collaboration.
• Healthcare professionals reported concerns about understanding AI outputs and rationale, variable confidence in AI accuracy, and differing views on AI's added value.

Conclusions:

• Significant issues exist regarding healthcare professionals' understanding of and trust in AI tools for clinical decision support.
• Opinions on the added value of AI tools varied widely among professionals.
• Concerted efforts are needed to optimize the integration of AI tools into real-world healthcare settings.