Accepted for/Published in: JMIR Human Factors
Date Submitted: Feb 25, 2021
Date Accepted: May 3, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Artificial Intelligence research trend in Human Factors Healthcare: A Mapping Review
ABSTRACT
Background:
While advancements have been made in artificial intelligence (AI) to develop prediction and classification models, little research has been devoted to real-world translations with a user-centered design approach. AI development studies in the healthcare context have often ignored the two critical factors: (a) Ecological validity and (b) Human cognition, creating challenges at the interface with clinicians and the clinical environment
Objective:
This literature review aimed to investigate the contributions made by major human factors communities in healthcare AI applications. This review also discusses emerging research gaps and provides future research directions to facilitate a safer integration of AI into the clinical workflow.
Methods:
We performed an extensive mapping review to capture all relevant articles published within the last ten years in the major human factors journals and conference proceedings listed in the “Human Factors and Ergonomics” category of the Scopus Master List. In each published volume, we searched for studies reporting qualitative or quantitative findings in the context of AI in healthcare. Studies were discussed based on the principles, such as evaluating workload, usability, trust in technology, perception, and user-centered design.
Results:
Forty-eight articles were included in the final review. Most studies emphasized on user perception, the usability of AI-based devices or technologies, cognitive workload, and user’s trust in AI. The review revealed a nascent but growing body of literature focusing on augmenting healthcare AI; however, little effort was noticed to ensure ecological validity with user-centered design approaches. Few studies, however, (n=5 against clinical/baseline standards, n=5 against clinicians) compared their AI models against a standard measure.
Conclusions:
Human factors researchers should actively be part of efforts in AI design and implementation as well as dynamical assessment of AI systems' effect on interaction, workflow, and patient outcomes. An AI system is part of a greater socio-technical system. Investigators with HFE expertise are essential when defining the dynamic interaction of AI within each element, process, and result of the work system.
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