Published on in Vol 6, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36501, first published .
Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence–Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation

Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence–Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation

Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence–Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation

Journals

  1. Khan N, Nwafor Okoli C, Ekpin V, Attai K, Chukwudi N, Sabi H, Akwaowo C, Osuji J, Benavente L, Uzoka F. Adoption and utilization of medical decision support systems in the diagnosis of febrile Diseases: A systematic literature review. Expert Systems with Applications 2023;220:119638 View
  2. Gould D, Dowsey M, Spelman T, Bailey J, Bunzli S, Rele S, Choong P. Established and Novel Risk Factors for 30-Day Readmission Following Total Knee Arthroplasty: A Modified Delphi and Focus Group Study to Identify Clinically Important Predictors. Journal of Clinical Medicine 2023;12(3):747 View
  3. Fröhlich P, Mirnig A, Falcioni D, Schrammel J, Diamond L, Fischer I, Tscheligi M. Effects of reliability indicators on usage, acceptance and preference of predictive process management decision support systems. Quality and User Experience 2022;7(1) View
  4. Fernando M, Abell B, Tyack Z, Donovan T, McPhail S, Naicker S. Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review. Journal of Medical Internet Research 2023;25:e45163 View
  5. Gould D, Bailey J, Spelman T, Bunzli S, Dowsey M, Choong P. Predicting 30-day readmission following total knee arthroplasty using machine learning and clinical expertise applied to clinical administrative and research registry data in an Australian cohort. Arthroplasty 2023;5(1) View
  6. Mittermaier M, Raza M, Kvedar J. Collaborative strategies for deploying AI-based physician decision support systems: challenges and deployment approaches. npj Digital Medicine 2023;6(1) View
  7. Levy J, Madrigal E, Vaickus L. Editorial: Artificial intelligence: applications in clinical medicine. Frontiers in Medical Technology 2023;5 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. Lareyre F, Nasr B, Chaudhuri A, Di Lorenzo G, Carlier M, Raffort J. Comprehensive Review of Natural Language Processing (NLP) in Vascular Surgery. EJVES Vascular Forum 2023;60:57 View
  10. Dalvi-Esfahani M, Mosharaf-Dehkordi M, Leong L, Ramayah T, Jamal Kanaan-Jebna A. Exploring the drivers of XAI-enhanced clinical decision support systems adoption: Insights from a stimulus-organism-response perspective. Technological Forecasting and Social Change 2023;195:122768 View
  11. Liao X, Yao C, Zhang J, Liu L. Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review. Journal of Evidence-Based Medicine 2023;16(4):534 View
  12. van der Vegt A, Scott I, Dermawan K, Schnetler R, Kalke V, Lane P. Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework. Journal of the American Medical Informatics Association 2023;30(9):1503 View
  13. 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
  14. Stevens A, Stetson P. Theory of trust and acceptance of artificial intelligence technology (TrAAIT): An instrument to assess clinician trust and acceptance of artificial intelligence. Journal of Biomedical Informatics 2023;148:104550 View
  15. Stewart J, Freeman S, Eroglu E, Dumitrascu N, Lu J, Goudie A, Sprivulis P, Akhlaghi H, Tran V, Sanfilippo F, Celenza A, Than M, Fatovich D, Walker K, Dwivedi G. Attitudes towards artificial intelligence in emergency medicine. Emergency Medicine Australasia 2024;36(2):252 View
  16. Okada Y, Ning Y, Ong M. Explainable artificial intelligence in emergency medicine: an overview. Clinical and Experimental Emergency Medicine 2023;10(4):354 View
  17. Shevtsova D, Ahmed A, Boot I, Sanges C, Hudecek M, Jacobs J, Hort S, Vrijhoef H. Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study. JMIR Human Factors 2024;11:e47031 View
  18. 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
  19. Rony M, Kayesh I, Bala S, Akter F, Parvin M. Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study. Heliyon 2024;10(4):e25718 View

Books/Policy Documents

  1. Mouloudj K, LE V, Bouarar A, Bouarar A, Asanza D, Srivastava M. The Use of Artificial Intelligence in Digital Marketing. View
  2. Katebi M, Poshdar M, Babaeian Jelodar M, Zihayat Kermani M. CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. View
  3. Katebi M, Poshdar M, Babaeian Jelodar M, Zihayat Kermani M. CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. View