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

Clinical Information and Decision Making (1426) Artificial Intelligence (1409) Natural Language Processing (704) Decision Support for Health Professionals (1203) Computer-Aided Diagnosis (134) Simulation (104) Clinical Informatics (1045) Health Informatics Education and Training (55) Methods and Instruments in Medical Informatics (133)

Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33540, first published .
How Clinicians Perceive Artificial Intelligence–Assisted Technologies in Diagnostic Decision Making: Mixed Methods Approach

How Clinicians Perceive Artificial Intelligence–Assisted Technologies in Diagnostic Decision Making: Mixed Methods Approach

How Clinicians Perceive Artificial Intelligence–Assisted Technologies in Diagnostic Decision Making: Mixed Methods Approach

Authors of this article:

Hyeyoung Hah1 Author Orcid Image ;   Deana Shevit Goldin2 Author Orcid Image
Article Authors Cited by (22) Tweetations (13) Metrics

Journals

  1. 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
  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. Higgins O, Short B, Chalup S, Wilson R. Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: An integrative review. International Journal of Mental Health Nursing 2023;32(4):966 View
  4. Raymond L, Castonguay A, Doyon O, Paré G. Nurse practitioners' involvement and experience with AI-based health technologies: A systematic review. Applied Nursing Research 2022;66:151604 View
  5. Shamszare H, Choudhury A. Clinicians’ Perceptions of Artificial Intelligence: Focus on Workload, Risk, Trust, Clinical Decision Making, and Clinical Integration. Healthcare 2023;11(16):2308 View
  6. Schulz P, Lwin M, Kee K, Goh W, Lam T, Sung J. Modeling the influence of attitudes, trust, and beliefs on endoscopists’ acceptance of artificial intelligence applications in medical practice. Frontiers in Public Health 2023;11 View
  7. Zhang M, Scandiffio J, Younus S, Jeyakumar T, Karsan I, Charow R, Salhia M, Wiljer D. The Adoption of AI in Mental Health Care–Perspectives From Mental Health Professionals: Qualitative Descriptive Study. JMIR Formative Research 2023;7:e47847 View
  8. Bilika P, Stefanouli V, Strimpakos N, Kapreli E. Clinical reasoning using ChatGPT: Is it beyond credibility for physiotherapists use?. Physiotherapy Theory and Practice 2024;40(12):2943 View
  9. Theben A, Plamenova N, Freire A. The “new currency of the future”: a review of literature on the skills needs of the workforce in times of accelerated digitalisation. Management Review Quarterly 2023 View
  10. Guo L, Zhou C, Xu J, Huang C, Yu Y, Lu G. Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance. Journal of Imaging Informatics in Medicine 2024;37(3):922 View
  11. Wimbarti S, Kairupan B, Tallei T. Critical review of self‐diagnosis of mental health conditions using artificial intelligence. International Journal of Mental Health Nursing 2024;33(2):344 View
  12. Goh W, Chia K, Cheung M, Kee K, Lwin M, Schulz P, Chen M, Wu K, Ng S, Lui R, Ang T, Yeoh K, Chiu H, Wu D, Sung J. Risk Perception, Acceptance, and Trust of Using AI in Gastroenterology Practice in the Asia-Pacific Region: Web-Based Survey Study. JMIR AI 2024;3:e50525 View
  13. Alshehri S, Alahmari K, Alasiry A. A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals. Journal of Personalized Medicine 2024;14(4):354 View
  14. Yang Y, Ngai E, Wang L. Resistance to artificial intelligence in health care: Literature review, conceptual framework, and research agenda. Information & Management 2024;61(4):103961 View
  15. Kawahara T, Sumi Y. GPT-4/4V's performance on the Japanese National Medical Licensing Examination. Medical Teacher 2024:1 View
  16. Doyon O, Raymond L. Clinical reasoning and clinical judgment in nursing research: A bibliometric analysis. International Journal of Nursing Knowledge 2024 View
  17. Xin Teoh Y, Othmani A, Li Goh S, Usman J, Lai K. Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review. IEEE Access 2024;12:109080 View
  18. Zhu Y, Park H. Publication, Collaboration, Citation Performance, and Triple Helix Innovation Gene of Artificial Intelligence Research in the Communication Field: Comparing Asia to the Rest of the World. Journal of the Knowledge Economy 2024 View
  19. Li M, Xiong X, Xu B. Attitudes and perceptions of Chinese oncologists towards artificial intelligence in healthcare: a cross-sectional survey. Frontiers in Digital Health 2024;6 View
  20. Zhang R, Duan W, Flathmann C, McNeese N, Knijnenburg B, Freeman G. Verbal vs. Visual: How Humans Perceive and Collaborate with AI Teammates Using Different Communication Modalities in Various Human-AI Team Compositions. Proceedings of the ACM on Human-Computer Interaction 2024;8(CSCW2):1 View

Books/Policy Documents

  1. Sreeraj V, Parlikar R, Bagali K, Singh Shekhawat H, Venkatasubramanian G. Exploration of Artificial Intelligence and Blockchain Technology in Smart and Secure Healthcare. View