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

Digital Health Reviews (1289) Reviews (100) Artificial Intelligence (1371) Clinical Information and Decision Making (1407)

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 Hogg1, 2, 3 Author Orcid Image ;   Mohaimen Al-Zubaidy1, 2 Author Orcid Image ;   Technology Enhanced Macular Services Study Reference Group1 ;   James Talks2 Author Orcid Image ;   Alastair K Denniston4, 5 Author Orcid Image ;   Christopher J Kelly6 Author Orcid Image ;   Johann Malawana7, 8 Author Orcid Image ;   Chrysanthi Papoutsi9 Author Orcid Image ;   Marion Dawn Teare1 Author Orcid Image ;   Pearse A Keane3, 10 Author Orcid Image ;   Fiona R Beyer11 Author Orcid Image ;   Gregory Maniatopoulos1, 12 Author Orcid Image
Article Authors Cited by (35) 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 2024;15:100347 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
  10. Giddings R, Joseph A, Callender T, Janes S, van der Schaar M, Sheringham J, Navani N. Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review. The Lancet Digital Health 2024;6(2):e131 View
  11. Gunathilaka N, Gooden T, Cooper J, Flanagan S, Marshall T, Haroon S, D’Elia A, Crowe F, Jackson T, Nirantharakumar K, Greenfield S. Perceptions on artificial intelligence-based decision-making for coexisting multiple long-term health conditions: protocol for a qualitative study with patients and healthcare professionals. BMJ Open 2024;14(2):e077156 View
  12. 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
  13. Macdonald T, Dinnes J, Maniatopoulos G, Taylor-Phillips S, Shinkins B, Hogg J, Dunbar J, Solebo A, Sutton H, Attwood J, Pogose M, Given-Wilson R, Greaves F, Macrae C, Pearson R, Bamford D, Tufail A, Liu X, Denniston A. Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study. JMIR Research Protocols 2024;13:e50568 View
  14. Canfell O, Woods L, Meshkat Y, Krivit J, Gunashanhar B, Slade C, Burton-Jones A, Sullivan C. The Impact of Digital Hospitals on Patient and Clinician Experience: Systematic Review and Qualitative Evidence Synthesis. Journal of Medical Internet Research 2024;26:e47715 View
  15. Frost E, Bosward R, Aquino Y, Braunack-Mayer A, Carter S. Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods. International Journal of Medical Informatics 2024;186:105417 View
  16. Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering 2024;11(4):337 View
  17. Khan S, Hoodbhoy Z, Raja M, Kim J, Hogg H, Manji A, Gulamali F, Hasan A, Shaikh A, Tajuddin S, Khan N, Patel M, Balu S, Samad Z, Sendak M, Ni Z. Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review. PLOS Digital Health 2024;3(5):e0000514 View
  18. Lee L, Salami R, Martin H, Shantharam L, Thomas K, Ashworth E, Allan E, Yung K, Pauling C, Leyden D, Arthurs O, Shelmerdine S. “How I would like AI used for my imaging”: children and young persons’ perspectives. European Radiology 2024;34(12):7751 View
  19. Abujaber A, Nashwan A. Ethical framework for artificial intelligence in healthcare research: A path to integrity. World Journal of Methodology 2024;14(3) View
  20. Benabed A, Bujor D, Manita Bulareanu A, Constantin Ene A. The Role of AI for Business and Companies’ Leadership and Internationalization in Globalization: A Background with Analysis. Proceedings of the International Conference on Business Excellence 2024;18(1):268 View
  21. Kumar A, Upadhyay U. Ethical Implications in AI-Based Health Care Decision Making: A Critical Analysis. AI in Precision Oncology 2024;1(5):246 View
  22. Ayorinde A, Mensah D, Walsh J, Ghosh I, Ibrahim S, Hogg J, Peek N, Griffiths F. Health Care Professionals’ Experience of Using AI: Systematic Review With Narrative Synthesis. Journal of Medical Internet Research 2024;26:e55766 View
  23. Carter S, Popic D, Marinovich M, Carolan L, Houssami N. Women's views on using artificial intelligence in breast cancer screening: A review and qualitative study to guide breast screening services. The Breast 2024;77:103783 View
  24. Shelmerdine S. Rethinking our relationship with AI: for better or worse, richer or poorer?. European Radiology 2024 View
  25. Hogg H, Martindale A, Liu X, Denniston A. Clinical Evaluation of Artificial Intelligence-Enabled Interventions. Investigative Ophthalmology & Visual Science 2024;65(10):10 View
  26. Fernando M, Abell B, McPhail S, Tyack Z, Tariq A, Naicker S. Applying the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability Framework Across Implementation Stages to Identify Key Strategies to Facilitate Clinical Decision Support System Integration Within a Large Metropolitan Health Service: Interview and Focus Group Study. JMIR Medical Informatics 2024;12:e60402 View
  27. Shen G, Mullen D, DePuccio M, Kerrissey M. The Human–Technology Continuum. Quality Management in Health Care 2024 View
  28. Hoffman J, Hattingh L, Shinners L, Angus R, Richards B, Hughes I, Wenke R. Allied health professionals’ perceptions of artificial intelligence in the clinical setting: a cross sectional survey (Preprint). JMIR Formative Research 2024 View
  29. Misra R, Keane P, Hogg H. How should we train clinicians for artificial intelligence in healthcare?. Future Healthcare Journal 2024;11(3):100162 View
  30. Togher D, Dean G, Moon J, Mayola R, Medina A, Repec J, Meheux M, Mather S, Storey M, Rickaby S, Abubacker M, Shelmerdine S. Evolution of radiology staff perspectives during artificial intelligence (AI) implementation for expedited lung cancer triage. Clinical Radiology 2024 View
  31. Siddals S, Torous J, Coxon A. “It happened to be the perfect thing”: experiences of generative AI chatbots for mental health. npj Mental Health Research 2024;3(1) View
  32. Santamato V, Tricase C, Faccilongo N, Iacoviello M, Marengo A. Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach. Applied Sciences 2024;14(22):10144 View
  33. Badawy W, Shaban M. Exploring geriatric nurses' perspectives on the adoption of AI in elderly care a qualitative study. Geriatric Nursing 2025;61:41 View

Books/Policy Documents

  1. Najjar R. A Comprehensive Overview of Telemedicine [Working Title]. View
  2. Subías-Beltrán P, Pitarch C, Migliorelli C, Marte L, Galofré M, Orte S. AI - Ethical and Legal Challenges [Working Title]. View