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

Artificial Intelligence (1754) Adoption and Change Management of eHealth Systems (801) Machine Learning (1748) Decision Support for Health Professionals (1347) Focus Groups and Qualitative Research for Human Factors Research (1043) Clinical Information and Decision Making (1665)

Published on in Vol 23, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29301, first published .
Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study

Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study

Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study

Authors of this article:

Luisa Pumplun1 Author Orcid Image ;   Mariska Fecho1 Author Orcid Image ;   Nihal Wahl1 Author Orcid Image ;   Felix Peters1 Author Orcid Image ;   Peter Buxmann1 Author Orcid Image
Article Authors Cited by (32) Tweetations (8) Metrics

Journals

  1. Uren V, Edwards J. Technology readiness and the organizational journey towards AI adoption: An empirical study. International Journal of Information Management 2023;68:102588 View
  2. Wenderott K, Gambashidze N, Weigl M. Integration of Artificial Intelligence Into Sociotechnical Work Systems—Effects of Artificial Intelligence Solutions in Medical Imaging on Clinical Efficiency: Protocol for a Systematic Literature Review. JMIR Research Protocols 2022;11(12):e40485 View
  3. Chen M, Zhang B, Cai Z, Seery S, Gonzalez M, Ali N, Ren R, Qiao Y, Xue P, Jiang Y. Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey. Frontiers in Medicine 2022;9 View
  4. Starke G, Schmidt B, De Clercq E, Elger B. Explainability as fig leaf? An exploration of experts’ ethical expectations towards machine learning in psychiatry. AI and Ethics 2023;3(1):303 View
  5. Haight T, Eshaghi A. Deep Learning Algorithms for Brain Imaging. Neurology 2023;100(12):549 View
  6. Chen M, Zhang B, Cai Z, Seery S, Mendez M, Ali N, Ren R, Qiao Y, Xue P, Jiang Y. Physician and Medical Student Attitudes Toward Clinical Artificial Intelligence: A Systematic Review with Cross-Sectional Survey. SSRN Electronic Journal 2022 View
  7. Lambert S, Madi M, Sopka S, Lenes A, Stange H, Buszello C, Stephan A. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. npj Digital Medicine 2023;6(1) 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. Gillner S. We're implementing AI now, so why not ask us what to do? – How AI providers perceive and navigate the spread of diagnostic AI in complex healthcare systems. Social Science & Medicine 2024;340:116442 View
  10. Budiarto A, Tsang K, Wilson A, Sheikh A, Shah S. Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review. JMIR AI 2023;2:e46717 View
  11. Weik L, Fehring L, Mortsiefer A, Meister S. Big 5 Personality Traits and Individual- and Practice-Related Characteristics as Influencing Factors of Digital Maturity in General Practices: Quantitative Web-Based Survey Study. Journal of Medical Internet Research 2024;26:e52085 View
  12. Williams R, Anderson S, Cresswell K, Kannelønning M, Mozaffar H, Yang X. Domesticating AI in medical diagnosis. Technology in Society 2024;76:102469 View
  13. Shulha M, Hovdebo J, D’Souza V, Thibault F, Harmouche R. Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach. JMIR Formative Research 2024;8:e50475 View
  14. Naderalvojoud B, Curtin C, Yanover C, El-Hay T, Choi B, Park R, Tabuenca J, Reeve M, Falconer T, Humphreys K, Asch S, Hernandez-Boussard T. Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network. Journal of the American Medical Informatics Association 2024;31(5):1051 View
  15. Ling Kuo R, Freethy A, Smith J, Hill R, C J, Jerome D, Harriss E, Collins G, Tutton E, Furniss D. Stakeholder perspectives towards diagnostic artificial intelligence: a co-produced qualitative evidence synthesis. eClinicalMedicine 2024;71:102555 View
  16. Shankar S, Garcia R, Hellerstein J, Parameswaran A. "We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning. Proceedings of the ACM on Human-Computer Interaction 2024;8(CSCW1):1 View
  17. Alami H, Lehoux P, Papoutsi C, Shaw S, Fleet R, Fortin J. Understanding the integration of artificial intelligence in healthcare organisations and systems through the NASSS framework: a qualitative study in a leading Canadian academic centre. BMC Health Services Research 2024;24(1) View
  18. Botha N, Ansah E, Segbedzi C, Dumahasi V, Maneen S, Kodom R, Tsedze I, Akoto L, Atsu F. Artificial intelligent tools: evidence-mapping on the perceived positive effects on patient-care and confidentiality. BMC Digital Health 2024;2(1) View
  19. Wenderott K, Krups J, Zaruchas F, Weigl M. Effects of artificial intelligence implementation on efficiency in medical imaging—a systematic literature review and meta-analysis. npj Digital Medicine 2024;7(1) View
  20. Okwor I, Hitch G, Hakkim S, Akbar S, Sookhoo D, Kainesie J. Digital Technologies Impact on Healthcare Delivery: A Systematic Review of Artificial Intelligence (AI) and Machine-Learning (ML) Adoption, Challenges, and Opportunities. AI 2024;5(4):1918 View
  21. Botha N, Segbedzi C, Dumahasi V, Maneen S, Kodom R, Tsedze I, Akoto L, Atsu F, Lasim O, Ansah E. Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety. Archives of Public Health 2024;82(1) View
  22. Muyama L, Neuraz A, Coulet A. Machine learning approaches for the discovery of clinical pathways from patient data: A systematic review. Journal of Biomedical Informatics 2024;160:104746 View
  23. Choi E, Choi J, Yoo T. Automated and code-free development of a risk calculator using ChatGPT-4 for predicting diabetic retinopathy and macular edema without retinal imaging. International Journal of Retina and Vitreous 2025;11(1) View
  24. Kim G, Namkoong K. Developing the Digital Health Communication Maturity Model: Systematic Review. Journal of Medical Internet Research 2025;27:e68344 View
  25. Suárez D, Almeida F, Blanco V, Toledo P. KubePipe: a container-based high-level parallelization tool for scalable machine learning pipelines. The Journal of Supercomputing 2025;81(3) View
  26. Whitney C, Preis H, Vargas A. Anticipatory moral distress in machine learning-based clinical decision support tool development: A qualitative analysis. SSM - Qualitative Research in Health 2025;7:100540 View
  27. Shin H, Hamovitch E, Gatov E, MacKinnon M, Samawi L, Boateng R, Thorpe K, Barwick M, Osorio-Valencia J. The NASSS (Non-Adoption, Abandonment, Scale-Up, Spread and Sustainability) framework use over time: A scoping review. PLOS Digital Health 2025;4(3):e0000418 View
  28. Al‐Qudimat A, Alqudimat M, Singh K, Fares Z, Ismail M, Yasin H, Al‐Zoubi R, Aboumarzouk O. Perception and Knowledge of Hospital Workers Toward Using Artificial Intelligence: A Descriptive Study. Health Science Reports 2025;8(5) View
  29. Yip S, Ning S, Wong N, Chan J, Ng K, Kwok B, Anders R, Lam S. Leveraging machine learning in nursing: innovations, challenges, and ethical insights. Frontiers in Digital Health 2025;7 View

Books/Policy Documents

  1. Sharma A. Revolutionizing the Healthcare Sector with AI. View

Books/Policy Documents

  1. Saiz Manzanares M, Martínez Martín M, Escolar Llamazares M, Ortiz Huerta J, Santamaría Vázquez M, Mercado Val E, Marticorena Sánchez R, Arnáiz González Á, Díez Pastor J, Rodríguez Arribas S. Formación y especialización en atención temprana: uso de recursos tecnológicos y de inteligencia artificial. View
  2. Saiz Manzanares M, Martínez Martín M, Escolar Llamazares M, Ortiz Huerta J, Santamaría Vázquez M, Mercado Val E, Marticorena Sánchez R, Arnáiz González Á, Díez Pastor J, Rodríguez Arribas S. Training and specialisation in early intervention: use of technological resources and artificial intelligence. View