Published on in Vol 8, No 4 (2021): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26964, first published .
Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

Journals

  1. Barry B, Zhu X, Behnken E, Inselman J, Schaepe K, McCoy R, Rushlow D, Noseworthy P, Richardson J, Curtis S, Sharp R, Misra A, Akfaly A, Molling P, Bernard M, Yao X. Provider Perspectives on Artificial Intelligence–Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study. JMIR AI 2022;1(1):e41940 View
  2. van der Meijden S, de Hond A, Thoral P, Steyerberg E, Kant I, Cinà G, Arbous M. Intensive Care Unit Physicians’ Perspectives on Artificial Intelligence–Based Clinical Decision Support Tools: Preimplementation Survey Study. JMIR Human Factors 2023;10:e39114 View
  3. Ulloa M, Rothrock B, Ahmad F, Jacobs M. Invisible clinical labor driving the successful integration of AI in healthcare. Frontiers in Computer Science 2022;4 View
  4. Thieme A, Hanratty M, Lyons M, Palacios J, Marques R, Morrison C, Doherty G. Designing Human-centered AI for Mental Health: Developing Clinically Relevant Applications for Online CBT Treatment. ACM Transactions on Computer-Human Interaction 2023;30(2):1 View
  5. Zając H, Li D, Dai X, Carlsen J, Kensing F, Andersen T. Clinician-Facing AI in the Wild: Taking Stock of the Sociotechnical Challenges and Opportunities for HCI. ACM Transactions on Computer-Human Interaction 2023;30(2):1 View
  6. Ghosh P, Posner K, Hyland S, van Cleve W, Bristow M, Long D, Palla K, Nair B, Fong C, Pauldine R, Vavilala M, O'Hara K. Framing Machine Learning Opportunities for Hypotension Prediction in Perioperative Care: A Socio-technical Perspective. ACM Transactions on Computer-Human Interaction 2023;30(5):1 View
  7. He X, Zheng X, Ding H, Liu Y, Zhu H. AI-CDSS Design Guidelines and Practice Verification. International Journal of Human–Computer Interaction 2023:1 View
  8. Andersen T, Nunes F, Wilcox L, Coiera E, Rogers Y. Introduction to the Special Issue on Human-Centred AI in Healthcare: Challenges Appearing in the Wild. ACM Transactions on Computer-Human Interaction 2023;30(2):1 View
  9. Fischer A, Rietveld A, Teunissen P, Hoogendoorn M, Bakker P. What is the future of artificial intelligence in obstetrics? A qualitative study among healthcare professionals. BMJ Open 2023;13(10):e076017 View
  10. Fritz B, Pugazenthi S, Budelier T, Tellor Pennington B, King C, Avidan M, Abraham J. User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications. Anesthesia & Analgesia 2024;138(4):804 View
  11. Bergquist M, Rolandsson B, Gryska E, Laesser M, Hoefling N, Heckemann R, Schneiderman J, Björkman-Burtscher I. Trust and stakeholder perspectives on the implementation of AI tools in clinical radiology. European Radiology 2023;34(1):338 View
  12. Evans R, Bryant L, Russell G, Absolom K. Trust and acceptability of data-driven clinical recommendations in everyday practice: A scoping review. International Journal of Medical Informatics 2024;183:105342 View
  13. Nong P, Adler-Milstein J, Kardia S, Platt J. Public perspectives on the use of different data types for prediction in healthcare. Journal of the American Medical Informatics Association 2024;31(4):893 View
  14. Ewals L, Heesterbeek L, Yu B, van der Wulp K, Mavroeidis D, Funk M, Snijders C, Jacobs I, Nederend J, Pluyter J. The Impact of Expectation Management and Model Transparency on Radiologists’ Trust and Utilization of AI Recommendations for Lung Nodule Assessment on Computed Tomography: Simulated Use Study. JMIR AI 2024;3:e52211 View