{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:15:05Z","timestamp":1781615705766,"version":"3.54.5"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of interest within medical images is crucial but manually laborious and prone to interobserver and intraobserver biases. As such, deep learning approaches could provide automated solutions for such applications. However, the potential of these techniques is often undermined by challenges in reproducibility and generalizability, which are key barriers to their clinical adoption. This paper introduces the RIDGE checklist, a comprehensive framework designed to assess the Reproducibility, Integrity, Dependability, Generalizability, and Efficiency of deep learning-based medical image segmentation models. The RIDGE checklist is not just a tool for evaluation but also a guideline for researchers striving to improve the quality and transparency of their work. By adhering to the principles outlined in the RIDGE checklist, researchers can ensure that their developed segmentation models are robust, scientifically valid, and applicable in a clinical setting.<\/jats:p>","DOI":"10.1007\/s10278-024-01282-9","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T13:49:19Z","timestamp":1731937759000},"page":"2524-2536","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5673-8210","authenticated-orcid":false,"given":"Farhad","family":"Maleki","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linda","family":"Moy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reza","family":"Forghani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tapotosh","family":"Ghosh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katie","family":"Ovens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steve","family":"Langer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pouria","family":"Rouzrokh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bardia","family":"Khosravi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Ganjizadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Warren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roxana","family":"Daneshjou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mana","family":"Moassefi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atlas Haddadi","family":"Avval","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan","family":"Sotardi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neil","family":"Tenenholtz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felipe","family":"Kitamura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy","family":"Kline","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,18]]},"reference":[{"issue":"8","key":"1282_CR1","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1097\/PAS.0000000000001079","volume":"42","author":"FD Allard","year":"2018","unstructured":"Allard, F.D., Goldsmith, J.D., Ayata, G., Challies, T.L., Najarian, R.M., Nasser, I.A., Wang, H., Yee, E.U.: Intraobserver and interobserver variability in the assessment of dysplasia in ampullary mucosal biopsies. The American Journal of Surgical Pathology 42(8), 1095\u20131100 (2018)","journal-title":"The American Journal of Surgical Pathology"},{"issue":"2","key":"1282_CR2","doi-asserted-by":"publisher","first-page":"105","DOI":"10.17816\/DD60622","volume":"2","author":"NS Kulberg","year":"2021","unstructured":"Kulberg, N.S., Reshetnikov, R.V., Novik, V.P., Elizarov, A.B., Gusev, M.A., Gombolevskiy, V.A., Vladzymyrskyy, A.V., Morozov, S.P.: Inter-observer variability between readers of CT images: all for one and one for all. Digital Diagnostics 2(2), 105\u2013118 (2021)","journal-title":"Digital Diagnostics"},{"issue":"1","key":"1282_CR3","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1186\/s40658-022-00515-6","volume":"9","author":"EC Covert","year":"2022","unstructured":"Covert, E.C., Fitzpatrick, K., Mikell, J., Kaza, R.K., Millet, J.D., Barkmeier, D., Gemmete, J., Christensen, J., Schipper, M.J., Dewaraja, Y.K.: Intra-and inter operator variability in MRI-based manual segmentation of HCC lesions and its impact on dosimetry. EJNMMI Physics 9(1), 90 (2022)","journal-title":"EJNMMI Physics"},{"key":"1282_CR4","doi-asserted-by":"crossref","unstructured":"Schmidt, A., Morales-Alvarez, P., Molina, R.: Probabilistic modeling of inter- and intra-observer variability in medical image segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21097\u201321106 (2023)","DOI":"10.1109\/ICCV51070.2023.01929"},{"key":"1282_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12916-019-1426-2","volume":"17","author":"CJ Kelly","year":"2019","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., King, D.: Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine 17, 1\u20139 (2019)","journal-title":"BMC Medicine"},{"key":"1282_CR6","doi-asserted-by":"crossref","unstructured":"Maleki, F., Ovens, K., Gupta, R., Reinhold, C., Spatz, A., Forghani, R.: Generalizability of machine learning models: Quantitative evaluation of three methodological pitfalls. Radiology: Artificial Intelligence 5(1), 220028 (2022)","DOI":"10.1148\/ryai.220028"},{"key":"1282_CR7","doi-asserted-by":"crossref","unstructured":"Yu, A.C., Mohajer, B., Eng, J.: External validation of deep learning algorithms for radiologic diagnosis: a systematic review. Radiology: Artificial Intelligence 4(3), 210064 (2022)","DOI":"10.1148\/ryai.210064"},{"key":"1282_CR8","doi-asserted-by":"crossref","unstructured":"Hadjiiski, L., Cha, K., Chan, H., Drukker, K., Morra, L., Nappi, J.J., Sahiner, B., Yoshida, H., Chen, Q., Deserno, T.M., et al.: AAPM task group report 273: recommendations on best practices for AI and machine learning for computer aided diagnosis in medical imaging. Medical Physics 50(2), 1\u201324 (2023)","DOI":"10.1002\/mp.16188"},{"issue":"2","key":"1282_CR9","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1161\/CIRCULATIONAHA.114.014508","volume":"131","author":"GS Collins","year":"2015","unstructured":"Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G.: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) the TRIPOD statement. Circulation 131(2), 211\u2013219 (2015)","journal-title":"Circulation"},{"issue":"2","key":"1282_CR10","doi-asserted-by":"publisher","first-page":"100","DOI":"10.4103\/0976-500X.72352","volume":"1","author":"KF Schulz","year":"2010","unstructured":"Schulz, K.F., Altman, D.G., Moher, D.: CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Journal of Pharmacology and Pharmacotherapeutics 1(2), 100\u2013107 (2010)","journal-title":"Journal of Pharmacology and Pharmacotherapeutics"},{"issue":"1","key":"1282_CR11","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1093\/fampra\/cmh103","volume":"21","author":"PM Bossuyt","year":"2004","unstructured":"Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4\u201310 (2004)","journal-title":"Family practice"},{"key":"1282_CR12","doi-asserted-by":"crossref","unstructured":"Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020)","DOI":"10.1148\/ryai.2020200029"},{"issue":"2","key":"1282_CR13","first-page":"125","volume":"11","author":"A Buslaev","year":"2020","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Informa tion 11(2), 125 (2020)","journal-title":"Informa tion"},{"issue":"21","key":"1282_CR14","doi-asserted-by":"publisher","first-page":"4522","DOI":"10.1093\/bioinformatics\/btz259","volume":"35","author":"MD Bloice","year":"2019","unstructured":"Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522\u20134524 (2019)","journal-title":"Bioinformatics"},{"key":"1282_CR15","doi-asserted-by":"publisher","first-page":"105382","DOI":"10.1016\/j.compbiomed.2022.105382","volume":"144","author":"Y Chen","year":"2022","unstructured":"Chen, Y., Yang, X., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022)","journal-title":"Computers in Biology and Medicine"},{"key":"1282_CR16","doi-asserted-by":"publisher","first-page":"63482","DOI":"10.1109\/ACCESS.2020.2982390","volume":"8","author":"V Kumar","year":"2020","unstructured":"Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. IEEE Access 8, 63482\u201363496 (2020)","journal-title":"IEEE Access"},{"issue":"5","key":"1282_CR17","doi-asserted-by":"publisher","first-page":"1516","DOI":"10.3390\/s20051516","volume":"20","author":"S Almotairi","year":"2020","unstructured":"Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020)","journal-title":"Sensors"},{"key":"1282_CR18","doi-asserted-by":"crossref","unstructured":"Sander, J., Vos, B.D., I\u02c7sgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020)","DOI":"10.1038\/s41598-020-77733-4"},{"key":"1282_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chan, S., Chen, J., Chang, K., Lin, C.-Y., Pan, H., Lin, W., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of U-Net breast density segmentation method for fat-sat MR images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877\u2013887 (2021)","DOI":"10.1007\/s10278-021-00472-z"},{"issue":"5","key":"1282_CR20","doi-asserted-by":"publisher","first-page":"4701","DOI":"10.1016\/j.aej.2021.03.048","volume":"60","author":"WM Salama","year":"2021","unstructured":"Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated CNN approach. Alexandria Engineering Journal 60(5), 4701\u20134709 (2021)","journal-title":"Alexandria Engineering Journal"},{"issue":"3","key":"1282_CR21","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1007\/s10278-021-00459-w","volume":"34","author":"LB Sappa","year":"2021","unstructured":"Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: RetFluidNet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691\u2013704 (2021)","journal-title":"Journal of Digital Imaging"},{"key":"1282_CR22","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1007\/s10278-021-00516-4","volume":"34","author":"Y Cho","year":"2021","unstructured":"Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network\u2013corrected labeling in magnetic resonance imaging\u2013derived proton density fat fraction. Journal of Digital Imaging 34, 1225\u20131236 (2021)","journal-title":"Journal of Digital Imaging"},{"key":"1282_CR23","doi-asserted-by":"publisher","first-page":"107562","DOI":"10.1016\/j.patcog.2020.107562","volume":"110","author":"D Zhang","year":"2021","unstructured":"Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021)","journal-title":"Pattern Recognition"},{"key":"1282_CR24","doi-asserted-by":"crossref","unstructured":"Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24, pp. 109\u2013119 (2021). Springer","DOI":"10.1007\/978-3-030-87193-2_11"},{"issue":"1","key":"1282_CR25","doi-asserted-by":"publisher","first-page":"268","DOI":"10.3390\/s21010268","volume":"21","author":"Y Jalali","year":"2021","unstructured":"Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021)","journal-title":"Sensors"},{"key":"1282_CR26","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218 (2022). Springer","DOI":"10.1007\/978-3-031-25066-8_9"},{"issue":"3","key":"1282_CR27","doi-asserted-by":"publisher","first-page":"448","DOI":"10.3174\/ajnr.A7419","volume":"43","author":"L Zhao","year":"2022","unstructured":"Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448\u2013454 (2022)","journal-title":"American Journal of Neuroradiology"},{"key":"1282_CR28","doi-asserted-by":"crossref","unstructured":"Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022)","DOI":"10.1148\/ryai.210205"},{"key":"1282_CR29","doi-asserted-by":"crossref","unstructured":"Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengts son, T., Carano, R.A.: Joint MRI T1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662\u2013673 (2022)","DOI":"10.1148\/radiol.211528"},{"issue":"1","key":"1282_CR30","doi-asserted-by":"publisher","first-page":"3423","DOI":"10.1038\/s41467-022-30841-3","volume":"13","author":"SP Primakov","year":"2022","unstructured":"Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022)","journal-title":"Nature Communications"},{"key":"1282_CR31","doi-asserted-by":"crossref","unstructured":"Lin, Y., Lin, Y., Huang, Y., Ho, C., Chiang, H., Lu, H., Wang, C., Wang, J., Ng, S., Lai, C., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023)","DOI":"10.1186\/s13244-022-01356-8"},{"key":"1282_CR32","doi-asserted-by":"crossref","unstructured":"Yeung, M., Rundo, L., Nan, Y., Sala, E., Sch\u00f6nlieb, C., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739\u2013752 (2023)","DOI":"10.1007\/s10278-022-00735-3"},{"issue":"1","key":"1282_CR33","doi-asserted-by":"publisher","first-page":"2770","DOI":"10.1038\/s41598-023-29814-3","volume":"13","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging. Scientific Reports 13(1), 2770 (2023)","journal-title":"Scientific Reports"},{"issue":"4","key":"1282_CR34","doi-asserted-by":"publisher","first-page":"1752","DOI":"10.1002\/mp.13438","volume":"46","author":"X Ma","year":"2019","unstructured":"Ma, X., Hadjiiski, L.M., Wei, J., Chan, H., Cha, K.H., Cohan, R.H., Caoili, E.M., Samala, R., Zhou, C., Lu, Y.: U-Net based deep learning bladder segmentation in CT urography. Medical Physics 46(4), 1752\u20131765 (2019)","journal-title":"Medical Physics"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01282-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01282-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01282-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T01:05:19Z","timestamp":1757120719000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01282-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,18]]},"references-count":34,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["1282"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01282-9","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,18]]},"assertion":[{"value":"24 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This research does not involve human participants, their data or biological material.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable as this research does not involve human subjects.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The manuscript does not contain any individual person\u2019s data in any form (including individual details, images, or videos). All visualization content is anonymized and derived from public resources, ensuring that all data used is publicly available.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"FM, LM, PR, BK, AG, DW, RD, MM, AHA, SS, NT, and TK are members of the Machine Learning Education Subcommittee of Society for Imaging Informatics in Medicine (SIIM). LM is the Editor of Radiology with salary support from RSNA and serves on the Editorial Board of JMRI. LM has received grant support from the Siemens Research Grant, the Gordon and Betty Moore Foundation, the Mary Kay Foundation, Google, and NCI\/NIH. LM has received personal fees from Lunit Insight, ICAD, Guerbet, and Medscape and is on the Advisory Board for ICAD, Lunit, and Guerbet. LM holds stock options in Lunit and has been reimbursed for meeting and travel expenses by the British Society of Breast Radiology, the European Society of Breast Imaging, and the Korean Society of Radiology. LM is also a member of the ISMRM Board of Trustees and serves on the ACR Data Safety Monitoring Board. RF has had a research collaboration\/grant and has acted as a consultant and\/or speaker for Nuance Communications Inc., Canon Medical Systems Inc., and GE Healthcare. RF is also a co-investigator on a National Institutes of Health STTR grant subaward and a co-principal investigator on a National Science Foundation grant. FK is the Vice-chair of the SIIM Machine Learning Committee, a member of the RIC at RSNA, a member of the AI committee at RSNA, an Early Career Consultant to the Editor of Radiology, and an Associate Editor for Radiology: Artificial Intelligence. FK is also a consultant for MD.ai, a consultant for GE Healthcare, and a speaker for Sharing Progress in Cancer Care. The rest of the authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}