Leveraging artificial intelligence in radiology education: challenges and opportunities
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00330-023-10112-5.pdf
Reference9 articles.
1. Kroft LJM, van der Velden L, Giron IH, Roelofs JJH, de Roos A, Geleijns J (2019) Added value of ultra-low-dose computed tomography, dose equivalent to chest X-Ray radiography, for diagnosing chest pathology. J Thorac Imaging 34(3):179–186. https://doi.org/10.1097/rti.0000000000000404
2. van den Berk IAH, Lejeune EH, Kanglie M et al (2023) The yield of chest X-ray or ultra-low-dose chest-CT in emergency department patients suspected of pulmonary infection without respiratory symptoms or signs. Eur Radiol. https://doi.org/10.1007/s00330-023-09664-3
3. Chassagnon G, Billet N, Rutten C et al (2023) Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation. Eur Radiol. https://doi.org/10.1007/s00330-023-10043-1
4. Maleck M, Fischer MR, Kammer B et al (2001) Do computers teach better? A media comparison study for case-based teaching in radiology. Radiographics 21(4):1025–32. https://doi.org/10.1148/radiographics.21.4.g01jl091025
5. Sarkany D, Deitte L (2017) Providing feedback: practical skills and strategies. Acad Radiol. 24(6):740–6. https://doi.org/10.1016/j.acra.2016.11.023
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1. Preface: Artificial Intelligence and the Revolution of Oncological Imaging;Critical Reviews™ in Oncogenesis;2024
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