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

Artificial Intelligence (2237) Registered Report (815) Machine Learning (2163) Adoption and Change Management of eHealth Systems (924) Knowledge Translation and Implementation Science (544) Digital Health, Telehealth and e-Innovation in Clinical Settings (574) Reviews (142)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55897, first published .
Implementation of Machine Learning Applications in Health Care Organizations: Systematic Review of Empirical Studies

Implementation of Machine Learning Applications in Health Care Organizations: Systematic Review of Empirical Studies

Implementation of Machine Learning Applications in Health Care Organizations: Systematic Review of Empirical Studies

Authors of this article:

Luigi M Preti1 Author Orcid Image ;   Vittoria Ardito1 Author Orcid Image ;   Amelia Compagni1, 2 Author Orcid Image ;   Francesco Petracca1 Author Orcid Image ;   Giulia Cappellaro1, 2 Author Orcid Image
Article Authors Cited by (21) Tweetations Metrics

Journals

  1. Heffernan A, Ganguli R, Sears I, Stephen A, Heffernan D. Choice of Machine Learning Models Is Important to Predict Post-Operative Infections in Surgical Patients. Surgical Infections 2025;26(7):520 View
  2. Farahani S, Hejazi M, Moradizeyveh S, Di Ieva A, Fatemizadeh E, Liu S. Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis. Diagnostics 2025;15(7):797 View
  3. Boudreaux C, Wroe E, Thapa A, Abebe N, Akiteng A, Drown L, Gadewar A, Karmacharya B, Karki S, Mansoor M, Mutagaywa R, Mulenga B, Mutengerere A, Nollino L, Salvi D, Dagnaw W, Bukhman G, Mocumbi A, Adler A, Probandari A. Organization of services for severe chronic Noncommunicable diseases at first-level hospitals in nine lower-income countries: Results from a Baseline assessment of PEN-Plus initiation. PLOS Global Public Health 2025;5(5):e0004552 View
  4. Gadde A. AI in Healthcare: Revolutionizing Early Disease Detection and Personalized Treatment. European Journal of Computer Science and Information Technology 2025;13(26):24 View
  5. Nasef D, Nasef D, Sher M, Toma M. A Standardized Validation Framework for Clinically Actionable Healthcare Machine Learning with Knee Osteoarthritis Grading as a Case Study. Algorithms 2025;18(6):343 View
  6. Zhang X, Kong W, Shi R, Sun L, Xu M, Gong L. Data-driven trends in critical care informatics: a bibliometric analysis of global collaborations using the MIMIC database (2004–2024). Computers in Biology and Medicine 2025;195:110670 View
  7. Averbuch T, Asselbergs F, Vardas P, Van Spall H. Great debate: artificial intelligence will replace much of what cardiologists do. European Heart Journal 2025;46(37):3628 View
  8. Ardito V, Cappellaro G, Compagni A, Petracca F, Preti L. Adoption of artificial intelligence applications in clinical practice: Insights from a Survey of Healthcare Organizations in Lombardy, Italy. DIGITAL HEALTH 2025;11 View
  9. Ismail M, Barth H, Holmén M, Petersson L, Irgang L. Transforming towards AI-augmented Healthcare: Experiences of physicians in Sweden. Technovation 2025;148:103333 View
  10. Farahani S, Hejazi M, Tabassum M, Di Ieva A, Mahdavifar N, Liu S. Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis. European Radiology 2025 View
  11. Večurkovská I, Roškovičová V, Kaťuchová J. Challenges in the Clinical Application of Machine Learning for Pancreatic Cancer. Bratislava Medical Journal 2025;126(10):2437 View
  12. Huang C, Xu J, Qiu H, Yue Y. Developing Nurse‐Accessible Hypertension Prediction Tools for Low‐Income Populations: A Comparative Analysis of Machine Learning Algorithms With SHAP Interpretation. International Journal of Nursing Practice 2025;31(5) View
  13. De Silva A, Prabagar K. Artificial intelligence in gastroenterology: Enhancing clinical practice, managing challenges and exploring future directions. Artificial Intelligence in Gastroenterology 2025;6(2) View
  14. Ding N, Li Y, Zhao Z, Meng X, Sun M, Ren X, Wang Y. Differential diagnosis of eczema and psoriasis using routine clinical data and machine learning: development of a web-based tool in a multicenter outpatient cohort. Frontiers in Medicine 2025;12 View
  15. Jiang Y, Nie D, Zhang L, Tang X, Li H, Li L, He H, Liu Y, Mao W, Xiong Z, Jin C. Construction and validation of a predictive model for postoperative stent occlusion in patients undergoing iliac vein stenting based on an explainable machine learning model. Frontiers in Surgery 2025;12 View
  16. O’Regan P, Butt F, Carter L, Graham D, Le Blanc A, Hoskins R, Stephenson L, Patil A, Shabbir M, Eken D, Singh S, Villa A, Agnelli L, Damian S, Grave C, Pretelli G, Garralda E, Frost H, de Braud F, Freitas A, Dive C, Unsworth H. UpSMART: five years of digital innovation in cancer clinical research—achievements, challenges, and recommendations. Frontiers in Digital Health 2025;7 View
  17. Eskandar K. Real-world deployment of machine learning models for opioid overdose and opioid use disorder: a systematic review of clinical and operational lessons for addiction medicine. Journal of Addictive Diseases 2026:1 View
  18. Olivieri L, Montanaro C, Pelizza A. Hypothetical enrollment – an anticipatory situated method to assess the implementation of AI diagnostics in clinical settings. Journal of Workplace Learning 2026:1 View
  19. Maynard S, Farrington J, Raza S, Stanworth S. Artificial Intelligence Implementation in Transfusion Medicine: Addressing the Challenges of Clinical Adoption. Transfusion Medicine Reviews 2026:150961 View
  20. Kato R, Obbo A, Kimera R. Predicting multi-factor authentication uptake using machine learning and the UTAUT Framework. Academia AI and Applications 2026;2(1) View

Conference Proceedings

  1. Shravani K, Priyanka M, Bhargav A, Sandhya P, Vazrala S, K.S.Kannan . 2025 4th International Conference on Automation, Computing and Renewable Systems (ICACRS). A Taxonomical Review of Machine Learning Paradigms in Medical Care View