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Review
. 2021 Jun 18;8(2):e28236.
doi: 10.2196/28236.

Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review

Onur Asan  1 , Avishek Choudhury  1
Affiliations
Review

Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review

Onur Asan et al. JMIR Hum Factors. .

Abstract

Background: Despite advancements 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 health care context have often ignored two critical factors of ecological validity and human cognition, creating challenges at the interface with clinicians and the clinical environment.

Objective: The aim of this literature review was to investigate the contributions made by major human factors communities in health care AI applications. This review also discusses emerging research gaps, and provides future research directions to facilitate a safer and user-centered integration of AI into the clinical workflow.

Methods: We performed an extensive mapping review to capture all relevant articles published within the last 10 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 health care. Studies are discussed based on the key 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 of the studies emphasized 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 health care AI; however, little effort has been made to ensure ecological validity with user-centered design approaches. Moreover, few studies (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 dynamic assessments of AI systems' effects on interaction, workflow, and patient outcomes. An AI system is part of a greater sociotechnical system. Investigators with human factors and ergonomics expertise are essential when defining the dynamic interaction of AI within each element, process, and result of the work system.

Keywords: artificial intelligence; ecological validity; health care systems; human factors; perception; trust; usability; workload.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
User-artificial intelligence (AI) interaction loops.
Figure 2
Figure 2
Illustrating some of the research objectives of experts in human factors and artificial intelligence.
Figure 3
Figure 3
Selection and exclusion process. AI: artificial intelligence.
Figure 4
Figure 4
Overview of selected publications and their venues.
See this image and copyright information in PMC

References

    1. Wang P. On defining artificial intelligence. J Artif Gen Intell. 2019;10(2):1–37. doi: 10.2478/jagi-2019-0002. - DOI
    1. Leão C, Gonçalves P, Cepeda T, Botelho L, Silva C. Study of the knowledge and impact of artificial intelligence on an academic community. International Conference on Intelligent Systems (IS); 2018; Funchal. 2018. Sep 25, pp. 891–895. - DOI
    1. McCarthy J, Hayes P. Some philosophical problems from the standpoint of artificial intelligence. In: Meltzer B, Michie D, editors. Machine Intelligence 4. Edinburgh, UK: Edinburgh University Press; 1981. pp. 463–502.
    1. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J. Scikit-learn: machine learning in Python (decision trees) J Mach Learn Res. 2011;12(1):2825–2830.
    1. Helm JM, Swiergosz AM, Haeberle HS, Karnuta JM, Schaffer JL, Krebs VE, Spitzer AI, Ramkumar PN. Machine learning and artificial intelligence: definitions, applications, and future directions. Curr Rev Musculoskelet Med. 2020 Feb;13(1):69–76. doi: 10.1007/s12178-020-09600-8. http://europepmc.org/abstract/MED/31983042 - DOI - PMC - PubMed

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