Palestinian Study Examines AI Reliance Among Junior Doctors in Diagnostic Radiology — Implications for Medical Training Worldwide

References

1. 1.Al-Quds University. (2024). AI Reliance in Diagnostic Radiology Among Intern Doctors in Palestine: A Triple-Arm, Triple-Blind, Parallel-Design Randomized Controlled Trial. ClinicalTrials.gov. https://clinicaltrials.gov/study/NCT07558746
2. 2.Beam, A. L., Manrai, A. K., & Ghassemi, M. (2020). Challenges to the reproducibility of machine learning models in health care. JAMA, 323(4), 305-306. https://doi.org/10.1001/jama.2020.2018
3. 3.Bluemke, D. A., Moy, L., Bredella, M. A., Ertl-Wagner, B. B., Fowler, K. J., Goh, V. J., Halpern, E. F., Hess, C. P., Schiebler, M. L., & Yucel, E. K. (2020). Assessing radiology research on artificial intelligence: A brief guide for authors, reviewers, and readers—From the Radiology Editorial Board. Radiology, 294(3), 487-489. https://doi.org/10.1148/radiol.2019192515
4. 4.Larson, D. B., Harvey, H., Rubin, D. L., Irani, N., Tse, J. R., & Langlotz, C. P. (2021). Regulatory frameworks for development and evaluation of artificial intelligence-based diagnostic imaging algorithms: Summary and recommendations. Journal of the American College of Radiology, 18(3), 413-424. https://doi.org/10.1016/j.jacr.2021.03.014
5. 5.Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31-38. https://doi.org/10.1038/s41591-021-01614-0