This paper is in the following e-collection/theme issue:

Digital Health Reviews (1309) Machine Learning (1486) Artificial Intelligence (1402) Clinical Information and Decision Making (1423) Decision Support for Health Professionals (1200)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38039, first published .
What Works Where and How for Uptake and Impact of Artificial Intelligence in Pathology: Review of Theories for a Realist Evaluation

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

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

Authors of this article:

Henry King1 Author Orcid Image ;   Judy Wright1 Author Orcid Image ;   Darren Treanor1, 2, 3, 4 Author Orcid Image ;   Bethany Williams2 Author Orcid Image ;   Rebecca Randell5, 6 Author Orcid Image
Article Authors Cited by (4) Tweetations (18) Metrics

Journals

  1. Sideris K, Weir C, Schmalfuss C, Hanson H, Pipke M, Tseng P, Lewis N, Sallam K, Bozkurt B, Hanff T, Schofield R, Larimer K, Kyriakopoulos C, Taleb I, Brinker L, Curry T, Knecht C, Butler J, Stehlik J. Artificial intelligence predictive analytics in heart failure: results of the pilot phase of a pragmatic randomized clinical trial. Journal of the American Medical Informatics Association 2024;31(4):919 View
  2. Foersch S, Schulz S. Künstliche Intelligenz in der Pathologie: Status quo und Zukunftsperspektiven. Die Onkologie 2024;30(5):362 View
  3. Haynes A, Loblay V. Rethinking Barriers and Enablers in Qualitative Health Research: Limitations, Alternatives, and Enhancements. Qualitative Health Research 2024;34(14):1371 View
  4. Foersch S, Schulz S. Künstliche Intelligenz in der Pathologie: Status quo und Zukunftsperspektiven. best practice onkologie 2024;19(5):182 View