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

Reviews on Human Factors (52) Artificial Intelligence (1371) Decision Support for Health Professionals (1185) Design and Usability of Clinical Software and EHRs (268) Clinical Information and Decision Making (1407) Machine Learning (1465) Digital Health, Telehealth and e-Innovation in Clinical Settings (401) Design of Processes and Workflows (102) Organizational Issues (154) Health Professionals' Training in eHealth, Digital Medicine, Medical Informatics (104)

Published on in Vol 9, No 1 (2022): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28639, first published .
Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence–Enabled Clinical Decision Support Systems: Literature Review

Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence–Enabled Clinical Decision Support Systems: Literature Review

Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence–Enabled Clinical Decision Support Systems: Literature Review

Authors of this article:

Michael Knop1 Author Orcid Image ;   Sebastian Weber1 Author Orcid Image ;   Marius Mueller1 Author Orcid Image ;   Bjoern Niehaves1 Author Orcid Image
Article Authors Cited by (28) Tweetations (2) Metrics

Journals

  1. Jones E, Tignanelli C. Postoperative Intensive Care Unit Overtriage. Annals of Surgery 2023;277(2):186 View
  2. Xue P, Si M, Qin D, Wei B, Seery S, Ye Z, Chen M, Wang S, Song C, Zhang B, Ding M, Zhang W, Bai A, Yan H, Dang L, Zhao Y, Rezhake R, Zhang S, Qiao Y, Qu Y, Jiang Y. Unassisted Clinicians Versus Deep Learning–Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis. Journal of Medical Internet Research 2023;25:e43832 View
  3. Wu Y, Mascaro S, Bhuiyan M, Fathima P, Mace A, Nicol M, Richmond P, Kirkham L, Dymock M, Foley D, McLeod C, Borland M, Martin A, Williams P, Marsh J, Snelling T, Blyth C, Pitzer V. Predicting the causative pathogen among children with pneumonia using a causal Bayesian network. PLOS Computational Biology 2023;19(3):e1010967 View
  4. Miller L, Bhattacharyya D, Miller V, Bhattacharyya M. Recent Trend in Artificial Intelligence-Assisted Biomedical Publishing: A Quantitative Bibliometric Analysis. Cureus 2023 View
  5. Dosyn D, Yatsenko A, Kovalevych V, Daradkeh Y. Application of automated planning technologies for completing the medical knowledge base. Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 2022;12:177 View
  6. Khalifa A, Obeid J, Erno J, Rockey D. The role of artificial intelligence in hepatology research and practice. Current Opinion in Gastroenterology 2023;39(3):175 View
  7. Bienefeld N, Kolbe M, Camen G, Huser D, Buehler P. Human-AI teaming: leveraging transactive memory and speaking up for enhanced team effectiveness. Frontiers in Psychology 2023;14 View
  8. Susanto A, Lyell D, Widyantoro B, Berkovsky S, Magrabi F. Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review. Journal of the American Medical Informatics Association 2023;30(12):2050 View
  9. Innes A, Martinez A, Gao X, Dinh N, Hoang G, Nguyen T, Vu V, Luu T, Le T, Lebrun V, Trieu V, Tran N, Qin Z, Pham H, Dinh V, Nguyen B, Truong T, Nguyen V, Nguyen V, Mai T. Computer-Aided Detection for Chest Radiography to Improve the Quality of Tuberculosis Diagnosis in Vietnam’s District Health Facilities: An Implementation Study. Tropical Medicine and Infectious Disease 2023;8(11):488 View
  10. Bellini V, Cussigh G, Bignami E. Artificial intelligence in anesthesia: an uphill but inevitable road. Canadian Journal of Anesthesia/Journal canadien d'anesthésie 2023;70(10):1703 View
  11. Küper A, Lodde G, Livingstone E, Schadendorf D, Krämer N. Mitigating cognitive bias with clinical decision support systems: an experimental study. Journal of Decision Systems 2024;33(3):439 View
  12. Vijayakumar S, Lee V, Leong Q, Hong S, Blasiak A, Ho D. Physicians’ Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform. JMIR Human Factors 2023;10:e48476 View
  13. Wang L, Zhang Z, Wang D, Cao W, Zhou X, Zhang P, Liu J, Fan X, Tian F. Human-centered design and evaluation of AI-empowered clinical decision support systems: a systematic review. Frontiers in Computer Science 2023;5 View
  14. Cioffi G, Pinilla-Echeverri N, Sheth T, Sibbald M. Does artificial intelligence enhance physician interpretation of optical coherence tomography: insights from eye tracking. Frontiers in Cardiovascular Medicine 2023;10 View
  15. Meeus M, Beirnaert C, Mahieu L, Laukens K, Meysman P, Mulder A, Van Laere D. Clinical Decision Support for Improved Neonatal Care: The Development of a Machine Learning Model for the Prediction of Late-onset Sepsis and Necrotizing Enterocolitis. The Journal of Pediatrics 2024;266:113869 View
  16. 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
  17. Weber S, Wyszynski M, Godefroid M, Plattfaut R, Niehaves B. How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey. Computational and Structural Biotechnology Journal 2024;24:146 View
  18. Dingel J, Kleine A, Cecil J, Sigl A, Lermer E, Gaube S. Predictors of Health Care Practitioners’ Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology. Journal of Medical Internet Research 2024;26:e57224 View
  19. Küper A, Krämer N. Psychological Traits and Appropriate Reliance: Factors Shaping Trust in AI. International Journal of Human–Computer Interaction 2024:1 View
  20. Abubakar A, Gupta D, Parida S. A Reinforcement Learning Approach for Intelligent Conversational Chatbot For Enhancing Mental Health Therapy. Procedia Computer Science 2024;235:916 View
  21. Buzzaccarini G, De Rosa L, Pagliardini L. Response to “Letter to the Editor: The Promise and Pitfalls of AI-Generated Anatomical Images: Evaluating Midjourney for Aesthetic Surgery Applications”. Aesthetic Plastic Surgery 2024 View
  22. Chau M. Ethical, legal, and regulatory landscape of artificial intelligence in Australian healthcare and ethical integration in radiography: A narrative review. Journal of Medical Imaging and Radiation Sciences 2024;55(4):101733 View
  23. Khorev V, Kiselev A, Badarin A, Antipov V, Drapkina O, Kurkin S, Hramov A. Review on the use of AI-based methods and tools for treating mental conditions and mental rehabilitation. The European Physical Journal Special Topics 2024 View
  24. Giebel G, Raszke P, Nowak H, Palmowski L, Adamzik M, Heinz P, Tokic M, Timmesfeld N, Brunkhorst F, Wasem J, Blase N. Problems and Barriers Related to the Use of AI-based CDSS: An Interview Study (Preprint). Journal of Medical Internet Research 2024 View
  25. Haga S. Artificial intelligence, medications, pharmacogenomics, and ethics. Pharmacogenomics 2024:1 View

Books/Policy Documents

  1. Dosyn D. Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems. View
  2. Hutson J, Plate D. Generative AI in Teaching and Learning. View