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Physica Medica : PM : An International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB), 36, 12\u201323. https:\/\/doi.org\/10.1016\/j.ejmp.2017.02.009.","DOI":"10.1016\/j.ejmp.2017.02.009"},{"issue":"7","key":"10.1016\/j.eswa.2025.130031_b0220","doi-asserted-by":"crossref","DOI":"10.2196\/26151","article-title":"Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study","volume":"23","author":"Nikolov","year":"2021","journal-title":"Journal of Medical Internet Research"},{"issue":"8","key":"10.1016\/j.eswa.2025.130031_b0225","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1007\/s11548-022-02830-w","article-title":"Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network","volume":"18","author":"Pankert","year":"2023","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"10.1016\/j.eswa.2025.130031_b0230","article-title":"The translation of in-house imaging AI research into a medical device ensuring ethical and regulatory integrity","volume":"182","author":"Pesapane","year":"2024","journal-title":"European Journal of Radiology"},{"issue":"1","key":"10.1016\/j.eswa.2025.130031_b0235","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1148\/radiol.2020200038","article-title":"Continuous Learning AI in Radiology: Implementation Principles and Early applications","volume":"297","author":"Pianykh","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.eswa.2025.130031_b0240","doi-asserted-by":"crossref","unstructured":"Puggelli,L., Uccheddu,F., Volpe,Y., Furferi,R., & Di Feo,D. 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