What Works Where and How for Uptake and Impact of Artificial Intelligence in Pathology: Review of Theories for a Realist Evaluation (Preprint)

Abstract

BACKGROUND There is increasing interest in use of artificial intelligence in pathology to increase accuracy and efficiency. To date, studies of clinicians’ perceptions of artificial intelligence have found only moderate acceptability, suggesting the need for further research regarding how to integrate it into clinical practice. OBJECTIVE To determine contextual factors that may support or constrain the uptake of artificial intelligence 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 artificial intelligence can provide benefit in pathology. 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 grey literature. In line with a realist approach, we also made use of relevant theory. RESULTS One hundred and one 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 centres is in reducing workload. For uptake of artificial intelligence, pathologist trust is essential. Existing theories suggest that if pathologists are able to ‘make sense’ of AI, engaged in the adoption process, supported in adapting their work processes, and can identify potential benefits to its introduction, it is more likely to be accepted. CONCLUSIONS For uptake of artificial intelligence 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 centres, 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.