King, H., Wright, J. orcid.org/0000-0002-5239-0173, Treanor, D. orcid.org/0000-0002-4579-484X et al. (2 more authors) (2023) What Works Where and How for Uptake and Impact of Artificial Intelligence in Pathology: Review of Theories for a Realist Evaluation. Journal of Medical Internet Research, 25. e38039. ISSN 1438-8871
Abstract
Background: There is increasing interest in the use of artificial intelligence (AI) in pathology to increase accuracy and efficiency. To date, studies of clinicians’ perceptions of AI have found only moderate acceptability, suggesting the need for further research regarding how to integrate it into clinical practice.
Objective: The aim of the study was to determine contextual factors that may support or constrain the uptake of AI in pathology.
Methods: To go beyond a simple listing of barriers and facilitators, we drew on the approach of realist evaluation and undertook a review of the literature to elicit stakeholders’ theories of how, for whom, and in what circumstances AI can provide benefit in pathology. Searches were designed by an information specialist and peer-reviewed by a second information specialist. Searches were run on the arXiv.org repository, MEDLINE, and the Health Management Information Consortium, with additional searches undertaken on a range of websites to identify gray literature. In line with a realist approach, we also made use of relevant theory. Included documents were indexed in NVivo 12, using codes to capture different contexts, mechanisms, and outcomes that could affect the introduction of AI in pathology. Coded data were used to produce narrative summaries of each of the identified contexts, mechanisms, and outcomes, which were then translated into theories in the form of context-mechanism-outcome configurations.
Results: A total of 101 relevant documents were identified. Our analysis indicates that the benefits that can be achieved will vary according to the size and nature of the pathology department’s workload and the extent to which pathologists work collaboratively; the major perceived benefit for specialist centers is in reducing workload. For uptake of AI, pathologists’ trust is essential. Existing theories suggest that if pathologists are able to “make sense” of AI, engage in the adoption process, receive support in adapting their work processes, and can identify potential benefits to its introduction, it is more likely to be accepted.
Conclusions: For uptake of AI in pathology, for all but the most simple quantitative tasks, measures will be required that either increase confidence in the system or provide users with an understanding of the performance of the system. For specialist centers, efforts should focus on reducing workload rather than increasing accuracy. Designers also need to give careful thought to usability and how AI is integrated into pathologists’ workflow.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © Henry King, Judy Wright, Darren Treanor, Bethany Williams, Rebecca Randell. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | artificial intelligence; AI; machine learning; histopathology; pathology; implementation; review |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Health Sciences (Leeds) > Academic Unit of Health Economics (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cancer and Pathology (LICAP) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Jul 2024 14:44 |
Last Modified: | 19 Jul 2024 14:44 |
Status: | Published |
Publisher: | JMIR Publications |
Identification Number: | 10.2196/38039 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214892 |