Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11547-024-01886-9.pdf
Reference73 articles.
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2. Erickson BJ, Kitamura F (2021) Magician’s corner: 9 performance metrics for machine learning models. Radiol Artif Intell 3:e200126. https://doi.org/10.1148/ryai.2021200126
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4. Park SY, Park JE, Kim H, Park SH (2021) Review of statistical methods for evaluating the performance of survival or other time-to-event prediction models (from conventional to deep learning approaches). Korean J Radiol 22:1697–1707. https://doi.org/10.3348/kjr.2021.0223
5. Chalkidou A, Shokraneh F, Kijauskaite G et al (2022) Recommendations for the development and use of imaging test sets to investigate the test performance of artificial intelligence in health screening. Lancet Digit Health 4:e899–e905. https://doi.org/10.1016/s2589-7500(22)00186-8
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