Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48249, first published .
Radiology Residents’ Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study

Radiology Residents’ Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study

Radiology Residents’ Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study

Journals

  1. Alkhatieb M, Subke A. Artificial Intelligence in Healthcare: A Study of Physician Attitudes and Perceptions in Jeddah, Saudi Arabia. Cureus 2024 View
  2. He C, Liu W, Xu J, Huang Y, Dong Z, Wu Y, Kharrazi H. Efficiency, accuracy, and health professional's perspectives regarding artificial intelligence in radiology practice: A scoping review. iRADIOLOGY 2024;2(2):156 View
  3. Ardila J, Sánchez S, Cadavid L, Rozo G, Romero J. Radiology Under Pressure: The Challenge of Burnout in Residents and a Call for Action. Academic Radiology 2024;31(7):3068 View
  4. Rajagopal A, Ayanian S, Ryu A, Qian R, Legler S, Peeler E, Issa M, Coons T, Kawamoto K. Machine Learning Operations in Health Care: A Scoping Review. Mayo Clinic Proceedings: Digital Health 2024;2(3):421 View
  5. Nelson A, Ivarsson A, Lydell M. Employability and long-term work life outcomes from studying at a Swedish university college: problematizing the notion of mismatch. Higher Education, Skills and Work-Based Learning 2024 View
  6. Tong W, Zhang X, Zeng H, Pan J, Gong C, Zhang H. Reforming China’s Secondary Vocational Medical Education: Adapting to the Challenges and Opportunities of the AI Era. JMIR Medical Education 2024;10:e48594 View
  7. Arkoh S, Akudjedu T, Amedu C, Antwi W, Elshami W, Ohene-Botwe B. Current Radiology workforce perspective on the integration of artificial intelligence in clinical practice: A systematic review. Journal of Medical Imaging and Radiation Sciences 2025;56(1):101769 View