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

Digital Health Reviews (992) Reviews (71) Artificial Intelligence (906) Clinical Information and Decision Making (1104)

Published on in Vol 25 (2023)

This is a member publication of University College London (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39742, first published .
Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

Authors of this article:

Henry David Jeffry Hogg 1, 2, 3 Author Orcid Image ;   Mohaimen Al-Zubaidy 1, 2 Author Orcid Image ;   Technology Enhanced Macular Services Study Reference Group 1 ;   James Talks 2 Author Orcid Image ;   Alastair K Denniston 4, 5 Author Orcid Image ;   Christopher J Kelly 6 Author Orcid Image ;   Johann Malawana 7, 8 Author Orcid Image ;   Chrysanthi Papoutsi 9 Author Orcid Image ;   Marion Dawn Teare 1 Author Orcid Image ;   Pearse A Keane 3, 10 Author Orcid Image ;   Fiona R Beyer 11 Author Orcid Image ;   Gregory Maniatopoulos 1, 12 Author Orcid Image
Article Authors Cited by (9) Tweetations (8) Metrics

Journals

  1. Hogg H, Brittain K, Teare D, Talks J, Balaskas K, Keane P, Maniatopoulos G. Safety and efficacy of an artificial intelligence-enabled decision tool for treatment decisions in neovascular age-related macular degeneration and an exploration of clinical pathway integration and implementation: protocol for a multi-methods validation study. BMJ Open 2023;13(2):e069443 View
  2. Bignami E, Vittori A, Lanza R, Compagnone C, Cascella M, Bellini V. The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN’s Challenge. Healthcare 2023;11(7):975 View
  3. Camaradou J, Hogg H. Commentary: Patient Perspectives on Artificial Intelligence; What have We Learned and How Should We Move Forward?. Advances in Therapy 2023;40(6):2563 View
  4. Gonzalez R, Saha A, Campbell C, Nejat P, Lokker C, Norgan A. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities. Journal of Pathology Informatics 2023:100347 View
  5. Nov O, Singh N, Mann D. Putting ChatGPT’s Medical Advice to the (Turing) Test: Survey Study. JMIR Medical Education 2023;9:e46939 View
  6. Sezgin E. Artificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers. DIGITAL HEALTH 2023;9 View
  7. Sallam M, Salim N, Barakat M, Al-Mahzoum K, Al-Tammemi A, Malaeb D, Hallit R, Hallit S. Assessing Health Students' Attitudes and Usage of ChatGPT in Jordan: Validation Study. JMIR Medical Education 2023;9:e48254 View
  8. Hogg H, Al-Zubaidy M, Keane P, Hughes G, Beyer F, Maniatopoulos G. Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research. Frontiers in Health Services 2023;3 View
  9. Wang S, Hogg H, Sangvai D, Patel M, Weissler E, Kellogg K, Ratliff W, Balu S, Sendak M. Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study. JMIR Formative Research 2023;7:e43963 View