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Quality of interaction between clinicians and artificial intelligence systems. A systematic review
Argyrios Perivolaris1,2, Chris Adams-McGavin3, Yasmine Madan4
1Institute of Medical Sciences, University of Toronto, Canada.
Future Healthcare Journal
|September 16, 2024
Summary
This study identified seven key categories for assessing artificial intelligence (AI) and clinician interactions. These findings provide a framework for evaluating AI
Area of Science:
- Medical Informatics
- Human-Computer Interaction
- Clinical Decision Support
Background:
- Artificial intelligence (AI) integration in healthcare offers potential quality improvements.
- Current AI evaluations primarily focus on model performance, neglecting AI-clinician interactions.
- A knowledge gap exists in assessing the quality of interactions between AI and clinicians.
Purpose of the Study:
- To systematically review literature and identify interaction traits for assessing AI-clinician interactions.
- To develop a foundational framework for evaluating the quality of AI-clinician relationships.
Main Methods:
- Systematic review of studies published up to June 2022 on clinician-AI interactions.
- Narrative synthesis of identified interaction traits due to study heterogeneity.
- Independent categorization of traits by two authors, followed by a consensus discussion.
Main Results:
- Identified 210 interaction traits from 34 studies, with 90 unique traits after deduplication.
- Common traits included usefulness, ease of use, trust, satisfaction, willingness to use, and usability.
- Classified unique traits into seven categories: usability/user experience, system performance, clinician trust/acceptance, impact on patient care, communication, ethical/professional concerns, and clinician engagement/workflow.
Conclusions:
- Seven categories of interaction traits between clinicians and AI systems were identified.
- These categories can form the basis of a framework for assessing AI-clinician interaction quality.
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