{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:17:46Z","timestamp":1787066266454,"version":"build-2736575974"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T00:00:00Z","timestamp":1753660800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T00:00:00Z","timestamp":1753660800000},"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":["Abdom Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>AI is rapidly transforming abdominal radiology. This scoping review mapped current applications across segmentation, detection, classification, prediction, and workflow optimization based on 432 studies published between 2019 and 2024. Most studies focused on CT imaging, with fewer involving MRI, ultrasound, or X-ray. Segmentation models (e.g., U-Net) performed well in liver and pancreatic imaging (Dice coefficient 0.65\u20130.90). Classification models (e.g., ResNet, DenseNet) were commonly used for diagnostic labeling, with reported sensitivities ranging from 52 to 100% and specificities from 40.7 to 99%. A small number of studies employed true object detection models (e.g., YOLOv3, YOLOv7, Mask R-CNN) capable of spatial lesion localization, marking an emerging trend toward localization-based AI. Predictive models demonstrated AUCs between 0.62 and 0.99 but often lacked interpretability and external validation. Workflow optimization studies reported improved efficiency (e.g., reduced report turnaround and scan repetition), though standardized benchmarks were often missing. Major gaps identified include limited real-world validation, underuse of non-CT modalities, and unclear regulatory pathways. Successful clinical integration will require robust validation, practical implementation, and interdisciplinary collaboration.<\/jats:p>","DOI":"10.1007\/s00261-025-05144-y","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T06:23:23Z","timestamp":1753683803000},"page":"1608-1617","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["From promise to practice: a scoping review of AI applications in abdominal radiology"],"prefix":"10.1007","volume":"51","author":[{"given":"Anastasia","family":"Fotis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neeraj","family":"Lalwani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pankaj","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judy","family":"Yee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,28]]},"reference":[{"key":"5144_CR1","doi-asserted-by":"publisher","unstructured":"Mervak, B.M., J.G. Fried, and A.P. Wasnik, A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging. Diagnostics (Basel), 2023. 13(18).https:\/\/doi.org\/10.3390\/diagnostics13182889","DOI":"10.3390\/diagnostics13182889"},{"issue":"1156","key":"5144_CR2","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1093\/bjr\/tqae035","volume":"97","author":"T Martin-Noguerol","year":"2024","unstructured":"Martin-Noguerol, T., P. Lopez-Ubeda, and A. Luna, Imagine there is no paperwork\u2026 it\u2019s easy if you try. Br J Radiol, 2024. 97(1156): p. 744\u2013746.https:\/\/doi.org\/10.1093\/bjr\/tqae035","journal-title":"Br J Radiol"},{"issue":"1","key":"5144_CR3","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1097\/RCT.0000000000001247","volume":"46","author":"AW Moawad","year":"2022","unstructured":"Moawad, A.W., et al., Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities. J Comput Assist Tomogr, 2022. 46(1): p. 78\u201390.https:\/\/doi.org\/10.1097\/RCT.0000000000001247","journal-title":"J Comput Assist Tomogr"},{"key":"5144_CR4","doi-asserted-by":"publisher","first-page":"103046","DOI":"10.1016\/j.media.2023.103046","volume":"92","author":"S Dayarathna","year":"2024","unstructured":"Dayarathna, S., et al., Deep learning based synthesis of MRI, CT and PET: Review and analysis. Med Image Anal, 2024. 92: p. 103046.https:\/\/doi.org\/10.1016\/j.media.2023.103046","journal-title":"Med Image Anal"},{"issue":"5","key":"5144_CR5","doi-asserted-by":"publisher","first-page":"3173","DOI":"10.1007\/s00330-021-08431-6","volume":"32","author":"M Gabelloni","year":"2022","unstructured":"Gabelloni, M., et al., Bridging gaps between images and data: a systematic update on imaging biobanks. Eur Radiol, 2022. 32(5): p. 3173\u20133186.https:\/\/doi.org\/10.1007\/s00330-021-08431-6","journal-title":"Eur Radiol"},{"issue":"4","key":"5144_CR6","doi-asserted-by":"publisher","first-page":"2791","DOI":"10.1007\/s00330-023-10217-x","volume":"34","author":"D Akinci","year":"2024","unstructured":"Akinci D\u2019Antonoli, T., et al., Reproducibility of radiomics quality score: an intra- and inter-rater reliability study. Eur Radiol, 2024. 34(4): p. 2791\u20132804https:\/\/doi.org\/10.1007\/s00330-023-10217-x","journal-title":"Eur Radiol"},{"issue":"3","key":"5144_CR7","doi-asserted-by":"publisher","first-page":"1701","DOI":"10.1007\/s00330-024-11341-y","volume":"35","author":"N Barry","year":"2025","unstructured":"Barry, N., et al., Evaluating the impact of the Radiomics Quality Score: a systematic review and meta-analysis. Eur Radiol, 2025. 35(3): p. 1701\u20131713https:\/\/doi.org\/10.1007\/s00330-024-11341-y","journal-title":"Eur Radiol"},{"key":"5144_CR8","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page, M.J., et al., The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 2021. 372: p. n71https:\/\/doi.org\/10.1136\/bmj.n71","journal-title":"BMJ"},{"key":"5144_CR9","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.mri.2022.05.002","volume":"91","author":"SL Saunders","year":"2022","unstructured":"Saunders, S.L., et al., Comparison of automatic liver volumetry performance using different types of magnetic resonance images. Magn Reson Imaging, 2022. 91: p. 16\u201323.https:\/\/doi.org\/10.1016\/j.mri.2022.05.002","journal-title":"Magn Reson Imaging"},{"issue":"7","key":"5144_CR10","doi-asserted-by":"publisher","first-page":"1148","DOI":"10.3390\/tomography10070087","volume":"10","author":"SJ Wang","year":"2024","unstructured":"Wang, S.J., et al., Automatically Detecting Pancreatic Cysts in Autosomal Dominant Polycystic Kidney Disease on MRI Using Deep Learning. Tomography, 2024. 10(7): p. 1148\u20131158.https:\/\/doi.org\/10.3390\/tomography10070087","journal-title":"Tomography"},{"issue":"7","key":"5144_CR11","doi-asserted-by":"publisher","first-page":"e","DOI":"10.1002\/acm2.13695","volume":"23","author":"Z Lin","year":"2022","unstructured":"Lin, Z., et al., Deep learning for emergency ascites diagnosis using ultrasonography images. J Appl Clin Med Phys, 2022. 23(7): p. ehttps:\/\/doi.org\/10.1002\/acm2.13695","journal-title":"J Appl Clin Med Phys"},{"key":"5144_CR12","doi-asserted-by":"publisher","first-page":"102109","DOI":"10.1016\/j.artmed.2021.102109","volume":"117","author":"PH Conze","year":"2021","unstructured":"Conze, P.H., et al., Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks. Artif Intell Med, 2021. 117: p. 102109.https:\/\/doi.org\/10.1016\/j.artmed.2021.102109","journal-title":"Artif Intell Med"},{"key":"5144_CR13","doi-asserted-by":"publisher","first-page":"105463","DOI":"10.1016\/jebiom2024105463","volume":"110","author":"J Sato","year":"2024","unstructured":"Sato, J., et al., Annotation-free multi-organ anomaly detection in abdominal CT using free-text radiology reports: a multi-centre retrospective study. EBioMedicine, 2024. 110: p. 105463https:\/\/doi.org\/10.1016\/j.ebiom.2024.105463","journal-title":"EBioMedicine"},{"issue":"6","key":"5144_CR14","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1093\/jamia\/ocaa342","volume":"28","author":"MT MacLean","year":"2021","unstructured":"MacLean, M.T., et al., Quantification of abdominal fat from computed tomography using deep learning and its association with electronic health records in an academic biobank. J Am Med Inform Assoc, 2021. 28(6): p. 1178\u20131187.https:\/\/doi.org\/10.1093\/jamia\/ocaa342","journal-title":"J Am Med Inform Assoc"},{"issue":"1161","key":"5144_CR15","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.1093\/bjr\/tqae122","volume":"97","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., et al., Preoperative prediction of renal fibrous capsule invasion in clear cell renal cell carcinoma using CT-based radiomics model. Br J Radiol, 2024. 97(1161): p. 1557\u20131567.https:\/\/doi.org\/10.1093\/bjr\/tqae122","journal-title":"Br J Radiol"},{"issue":"1","key":"5144_CR16","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s00261-023-04089-4","volume":"49","author":"J Chen","year":"2024","unstructured":"Chen, J., et al., Implications of ultrasound-based deep learning model for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Abdom Radiol (NY), 2024. 49(1): p. 93\u2013102.https:\/\/doi.org\/10.1007\/s00261-023-04089-4","journal-title":"Abdom Radiol (NY)"},{"issue":"10","key":"5144_CR17","doi-asserted-by":"publisher","first-page":"3189","DOI":"10.1007\/s00261-023-03988-w","volume":"48","author":"Y Singh","year":"2023","unstructured":"Singh, Y., et al., Deep learning approach for differentiating indeterminate adrenal masses using CT imaging. Abdom Radiol (NY), 2023. 48(10): p. 3189\u20133194.https:\/\/doi.org\/10.1007\/s00261-023-03988-w","journal-title":"Abdom Radiol (NY)"},{"key":"5144_CR18","doi-asserted-by":"publisher","first-page":"110216","DOI":"10.1016\/jejrad2022110216","volume":"150","author":"MW Brejnebol","year":"2022","unstructured":"Brejnebol, M.W., et al., Artificial Intelligence based detection of pneumoperitoneum on CT scans in patients presenting with acute abdominal pain: A clinical diagnostic test accuracy study. Eur J Radiol, 2022. 150: p. 110216https:\/\/doi.org\/10.1016\/j.ejrad.2022.110216","journal-title":"Eur J Radiol"},{"issue":"3","key":"5144_CR19","doi-asserted-by":"publisher","first-page":"1862","DOI":"10.1007\/s00330-022-09171-x","volume":"33","author":"Z Zhou","year":"2023","unstructured":"Zhou, Z., et al., Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease. Eur Radiol, 2023. 33(3): p. 1862\u20131872https:\/\/doi.org\/10.1007\/s00330-022-09171-x","journal-title":"Eur Radiol"},{"issue":"10","key":"5144_CR20","doi-asserted-by":"publisher","first-page":"4576","DOI":"10.1007\/s00261-021-03142-4","volume":"46","author":"SH Wang","year":"2021","unstructured":"Wang, S.H., et al., Automatic discrimination of different sequences and phases of liver MRI using a dense feature fusion neural network: a preliminary study. Abdom Radiol (NY), 2021. 46(10): p. 4576\u20134587.https:\/\/doi.org\/10.1007\/s00261-021-03142-4","journal-title":"Abdom Radiol (NY)"},{"issue":"1","key":"5144_CR21","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1148\/radiol.220171","volume":"306","author":"HJ Park","year":"2023","unstructured":"Park, H.J., et al., Deep Learning-based Detection of Solid and Cystic Pancreatic Neoplasms at Contrast-enhanced CT. Radiology, 2023. 306(1): p. 140\u2013149.https:\/\/doi.org\/10.1148\/radiol.220171","journal-title":"Radiology"},{"issue":"4","key":"5144_CR22","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1007\/s00261-023-04157-9","volume":"49","author":"Y Li","year":"2024","unstructured":"Li, Y., et al., Surgical prediction of neonatal necrotizing enterocolitis based on radiomics and clinical information. Abdom Radiol (NY), 2024. 49(4): p. 1020\u20131030.https:\/\/doi.org\/10.1007\/s00261-023-04157-9","journal-title":"Abdom Radiol (NY)"},{"issue":"10","key":"5144_CR23","doi-asserted-by":"publisher","first-page":"4045","DOI":"10.1016\/j.acra.2024.03.044","volume":"31","author":"BE Enchakalody","year":"2024","unstructured":"Enchakalody, B.E., et al., Local Assessment and Small Bowel Crohn\u2019s Disease Severity Scoring using AI. Acad Radiol, 2024. 31(10): p. 4045\u20134056.https:\/\/doi.org\/10.1016\/j.acra.2024.03.044","journal-title":"Acad Radiol"},{"issue":"1","key":"5144_CR24","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1186\/s40644-024-00809-1","volume":"24","author":"SH Chiu","year":"2024","unstructured":"Chiu, S.H., et al., Improving the prediction of patient survival with the aid of residual convolutional neural network (ResNet) in colorectal cancer with unresectable liver metastases treated with bevacizumab-based chemotherapy. Cancer Imaging, 2024. 24(1): p. 165.https:\/\/doi.org\/10.1186\/s40644-024-00809-1","journal-title":"Cancer Imaging"},{"key":"5144_CR25","doi-asserted-by":"publisher","first-page":"104409","DOI":"10.1016\/jcompbiomed2021104409","volume":"133","author":"S Amiri","year":"2021","unstructured":"Amiri, S., et al., Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models. Comput Biol Med, 2021. 133: p. 104409https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104409","journal-title":"Comput Biol Med"},{"issue":"1","key":"5144_CR26","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s00261-021-03308-0","volume":"47","author":"JS Ko","year":"2022","unstructured":"Ko, J.S., et al., Prediction of insufficient hepatic enhancement during the Hepatobiliary phase of Gd-EOB DTPA-enhanced MRI using machine learning classifier and feature selection algorithms. Abdom Radiol (NY), 2022. 47(1): p. 161\u2013173.https:\/\/doi.org\/10.1007\/s00261-021-03308-0","journal-title":"Abdom Radiol (NY)"},{"issue":"3","key":"5144_CR27","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/s11547-024-01779-x","volume":"129","author":"V Granata","year":"2024","unstructured":"Granata, V., et al., Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging. Radiol Med, 2024. 129(3): p. 420\u2013428.https:\/\/doi.org\/10.1007\/s11547-024-01779-x","journal-title":"Radiol Med"},{"issue":"2","key":"5144_CR28","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1002\/mp.13918","volume":"47","author":"AD Missert","year":"2020","unstructured":"Missert, A.D., et al., Synthesizing images from multiple kernels using a deep convolutional neural network. Med Phys, 2020. 47(2): p. 422\u2013430.https:\/\/doi.org\/10.1002\/mp.13918","journal-title":"Med Phys"},{"key":"5144_CR29","doi-asserted-by":"publisher","first-page":"106575","DOI":"10.1016\/j.cmpb.2021.106575","volume":"214","author":"Z Liu","year":"2022","unstructured":"Liu, Z., et al., Abdominal computed tomography localizer image generation: A deep learning approach. Comput Methods Programs Biomed, 2022. 214: p. 106575.https:\/\/doi.org\/10.1016\/j.cmpb.2021.106575","journal-title":"Comput Methods Programs Biomed"},{"key":"5144_CR30","doi-asserted-by":"publisher","first-page":"102539","DOI":"10.1016\/j.artmed.2023.102539","volume":"139","author":"T Intharah","year":"2023","unstructured":"Intharah, T., et al., BiTNet: Hybrid deep convolutional model for ultrasound image analysis of human biliary tract and its applications. Artif Intell Med, 2023. 139: p. 102539https:\/\/doi.org\/10.1016\/j.artmed.2023.102539","journal-title":"Artif Intell Med"},{"issue":"8","key":"5144_CR31","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1007\/s00247-024-05953-1","volume":"54","author":"DJ Shin","year":"2024","unstructured":"Shin, D.J., et al., Low-iodine-dose computed tomography coupled with an artificial intelligence-based contrast-boosting technique in children: a retrospective study on comparison with conventional-iodine-dose computed tomography. Pediatr Radiol, 2024. 54(8): p. 1315\u20131324.https:\/\/doi.org\/10.1007\/s00247-024-05953-1","journal-title":"Pediatr Radiol"},{"issue":"11","key":"5144_CR32","doi-asserted-by":"publisher","first-page":"5923","DOI":"10.1007\/s00330-020-07006-1","volume":"30","author":"ML Kromrey","year":"2020","unstructured":"Kromrey, M.L., et al., Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning-based filter using convolutional neural network. Eur Radiol, 2020. 30(11): p. 5923\u20135932.https:\/\/doi.org\/10.1007\/s00330-020-07006-1","journal-title":"Eur Radiol"},{"key":"5144_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-025-04990-0","author":"A Gupta","year":"2025","unstructured":"Gupta, A., et al., Applications of artificial intelligence in abdominal imaging. Abdom Radiol (NY), 2025.https:\/\/doi.org\/10.1007\/s00261-025-04990-0","journal-title":"Abdom Radiol (NY)"},{"key":"5144_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-025-11670-6","author":"SD Westfechtel","year":"2025","unstructured":"Westfechtel, S.D., et al., AI in motion: the impact of data augmentation strategies on mitigating MRI motion artifacts. Eur Radiol, 2025.https:\/\/doi.org\/10.1007\/s00330-025-11670-6","journal-title":"Eur Radiol"},{"issue":"1","key":"5144_CR35","doi-asserted-by":"publisher","first-page":"16791","DOI":"10.1038\/s41598-023-43715-5","volume":"13","author":"R Kushol","year":"2023","unstructured":"Kushol, R., et al., Effects of MRI scanner manufacturers in classification tasks with deep learning models. Sci Rep, 2023. 13(1): p. 16791.https:\/\/doi.org\/10.1038\/s41598-023-43715-5","journal-title":"Sci Rep"},{"issue":"2","key":"5144_CR36","doi-asserted-by":"publisher","first-page":"e","DOI":"10.1148\/radiol.220101","volume":"306","author":"C Robinson-Weiss","year":"2023","unstructured":"Robinson-Weiss, C., et al., Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT. Radiology, 2023. 306(2): p. ehttps:\/\/doi.org\/10.1148\/radiol.220101","journal-title":"Radiology"},{"issue":"7","key":"5144_CR37","doi-asserted-by":"publisher","first-page":"3260","DOI":"10.1007\/s00261-021-02981-5","volume":"46","author":"Y Zheng","year":"2021","unstructured":"Zheng, Y., et al., Deep learning with a convolutional neural network model to differentiate renal parenchymal tumors: a preliminary study. Abdom Radiol (NY), 2021. 46(7): p. 3260\u20133268.https:\/\/doi.org\/10.1007\/s00261-021-02981-5","journal-title":"Abdom Radiol (NY)"},{"issue":"2","key":"5144_CR38","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.jvscit.2022.04.003","volume":"8","author":"JR Camara","year":"2022","unstructured":"Camara, J.R., et al., Development of a convolutional neural network to detect abdominal aortic aneurysms. J Vasc Surg Cases Innov Tech, 2022. 8(2): p. 305\u2013311https:\/\/doi.org\/10.1016\/j.jvscit.2022.04.003","journal-title":"J Vasc Surg Cases Innov Tech"},{"issue":"6","key":"5144_CR39","doi-asserted-by":"publisher","first-page":"2729","DOI":"10.1007\/s10278-024-01164-0","volume":"37","author":"A Raj","year":"2024","unstructured":"Raj, A., et al., Streamlining Acute Abdominal Aortic Dissection Management-An AI-based CT Imaging Workflow. J Imaging Inform Med, 2024. 37(6): p. 2729\u20132739.https:\/\/doi.org\/10.1007\/s10278-024-01164-0","journal-title":"J Imaging Inform Med"},{"key":"5144_CR40","doi-asserted-by":"publisher","first-page":"100096","DOI":"10.1016\/jjvssci202211004","volume":"4","author":"RT Tomihama","year":"2023","unstructured":"Tomihama, R.T., J.R. Camara, and S.C. Kiang, Machine learning analysis of confounding variables of a convolutional neural network specific for abdominal aortic aneurysms. JVS Vasc Sci, 2023. 4: p. 100096https:\/\/doi.org\/10.1016\/j.jvssci.2022.11.004","journal-title":"JVS Vasc Sci"},{"key":"5144_CR41","doi-asserted-by":"publisher","first-page":"110231","DOI":"10.1016\/j.clinimag.2024.110231","volume":"113","author":"AP Wasnik","year":"2024","unstructured":"Wasnik, A.P., et al., Machine learning methods in automated detection of CT enterography findings in Crohn\u2019s disease: A feasibility study. Clin Imaging, 2024. 113: p. 110231https:\/\/doi.org\/10.1016\/j.clinimag.2024.110231","journal-title":"Clin Imaging"},{"issue":"7","key":"5144_CR42","doi-asserted-by":"publisher","first-page":"2187","DOI":"10.1007\/s00261-024-04307-7","volume":"49","author":"B Lu","year":"2024","unstructured":"Lu, B., et al., A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn\u2019s disease. Abdom Radiol (NY), 2024. 49(7): p. 2187\u20132197.https:\/\/doi.org\/10.1007\/s00261-024-04307-7","journal-title":"Abdom Radiol (NY)"},{"key":"5144_CR43","doi-asserted-by":"publisher","first-page":"111798","DOI":"10.1016\/j.ejrad.2024.111798","volume":"181","author":"S Katal","year":"2024","unstructured":"Katal, S., B. York, and A. Gholamrezanezhad, AI in radiology: From promise to practice - A guide to effective integration. Eur J Radiol, 2024. 181: p. 111798https:\/\/doi.org\/10.1016\/j.ejrad.2024.111798","journal-title":"Eur J Radiol"},{"issue":"11","key":"5144_CR44","doi-asserted-by":"publisher","first-page":"1491","DOI":"10.1016\/j.acra.2020.08.022","volume":"28","author":"RM Summers","year":"2021","unstructured":"Summers, R.M., et al., Atherosclerotic Plaque Burden on Abdominal CT: Automated Assessment With Deep Learning on Noncontrast and Contrast-enhanced Scans. Acad Radiol, 2021. 28(11): p. 1491\u20131499.https:\/\/doi.org\/10.1016\/j.acra.2020.08.022","journal-title":"Acad Radiol"},{"issue":"2","key":"5144_CR45","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1097\/RCT.0000000000001273","volume":"46","author":"T Masuda","year":"2022","unstructured":"Masuda, T., et al., Prediction of Aortic Contrast Enhancement on Dynamic Hepatic Computed Tomography-Performance Comparison of Machine Learning Methods and Simulation Software. J Comput Assist Tomogr, 2022. 46(2): p. 183\u2013189.https:\/\/doi.org\/10.1097\/RCT.0000000000001273","journal-title":"J Comput Assist Tomogr"},{"issue":"10","key":"5144_CR46","doi-asserted-by":"publisher","first-page":"3383","DOI":"10.1007\/s00261-024-04313-9","volume":"49","author":"J Li","year":"2024","unstructured":"Li, J., et al., Interpretable machine learning based on CT-derived extracellular volume fraction to predict pathological grading of hepatocellular carcinoma. Abdom Radiol (NY), 2024. 49(10): p. 3383\u20133396.https:\/\/doi.org\/10.1007\/s00261-024-04313-9","journal-title":"Abdom Radiol (NY)"},{"issue":"1","key":"5144_CR47","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1631\/jzus.B2300363","volume":"25","author":"Z Liu","year":"2024","unstructured":"Liu, Z., et al., Deep learning-based radiomics allows for a more accurate assessment of sarcopenia as a prognostic factor in hepatocellular carcinoma. J Zhejiang Univ Sci B, 2024. 25(1): p. 83\u201390.https:\/\/doi.org\/10.1631\/jzus.B2300363","journal-title":"J Zhejiang Univ Sci B"},{"key":"5144_CR48","doi-asserted-by":"publisher","first-page":"111590","DOI":"10.1016\/j.ejrad.2024.111590","volume":"177","author":"M Ce","year":"2024","unstructured":"Ce, M., et al., Radiologists\u2019 perceptions on AI integration: An in-depth survey study. Eur J Radiol, 2024. 177: p. 111590.https:\/\/doi.org\/10.1016\/j.ejrad.2024.111590","journal-title":"Eur J Radiol"},{"issue":"1150","key":"5144_CR49","doi-asserted-by":"publisher","first-page":"20221031","DOI":"10.1259\/bjr.20221031","volume":"96","author":"C Mello-Thoms","year":"2023","unstructured":"Mello-Thoms, C. and C.A.B. Mello, Clinical applications of artificial intelligence in radiology. Br J Radiol, 2023. 96(1150): p. 20221031https:\/\/doi.org\/10.1259\/bjr.20221031","journal-title":"Br J Radiol"},{"key":"5144_CR50","doi-asserted-by":"publisher","first-page":"112014","DOI":"10.1016\/j.ejrad.2025.112014","volume":"185","author":"F De-Giorgio","year":"2025","unstructured":"De-Giorgio, F., et al., The need for balancing \u2018black box\u2019 systems and explainable artificial intelligence: A necessary implementation in radiology. Eur J Radiol, 2025. 185: p. 112014https:\/\/doi.org\/10.1016\/j.ejrad.2025.112014","journal-title":"Eur J Radiol"},{"issue":"1","key":"5144_CR51","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1007\/s00330-023-09991-5","volume":"34","author":"KG van Leeuwen","year":"2024","unstructured":"van Leeuwen, K.G., et al., Clinical use of artificial intelligence products for radiology in the Netherlands between 2020 and 2022. Eur Radiol, 2024. 34(1): p. 348\u2013354.https:\/\/doi.org\/10.1007\/s00330-023-09991-5","journal-title":"Eur Radiol"},{"issue":"21","key":"5144_CR52","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.1056\/NEJMra2301725","volume":"388","author":"P Rajpurkar","year":"2023","unstructured":"Rajpurkar, P. and M.P. Lungren, The Current and Future State of AI Interpretation of Medical Images. N Engl J Med, 2023. 388(21): p. 1981\u20131990.https:\/\/doi.org\/10.1056\/NEJMra2301725","journal-title":"N Engl J Med"},{"issue":"11","key":"5144_CR53","doi-asserted-by":"publisher","first-page":"7998","DOI":"10.1007\/s00330-022-08784-6","volume":"32","author":"BS Kelly","year":"2022","unstructured":"Kelly, B.S., et al., Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE). Eur Radiol, 2022. 32(11): p. 7998\u20138007.https:\/\/doi.org\/10.1007\/s00330-022-08784-6","journal-title":"Eur Radiol"},{"issue":"1","key":"5144_CR54","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1093\/jamia\/ocae259","volume":"32","author":"MR Bujotzek","year":"2025","unstructured":"Bujotzek, M.R., et al., Real-world federated learning in radiology: hurdles to overcome and benefits to gain. J Am Med Inform Assoc, 2025. 32(1): p. 193\u2013205.https:\/\/doi.org\/10.1093\/jamia\/ocae259","journal-title":"J Am Med Inform Assoc"},{"key":"5144_CR55","doi-asserted-by":"publisher","first-page":"108768","DOI":"10.1016\/j.cmpb.2025.108768","volume":"267","author":"A Raza","year":"2025","unstructured":"Raza, A., et al., Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis. Comput Methods Programs Biomed, 2025. 267: p. 108768.https:\/\/doi.org\/10.1016\/j.cmpb.2025.108768","journal-title":"Comput Methods Programs Biomed"},{"issue":"6","key":"5144_CR56","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1016\/j.rcl.2021.07.007","volume":"59","author":"HB Harvey","year":"2021","unstructured":"Harvey, H.B. and V. Gowda, Regulatory Issues and Challenges to Artificial Intelligence Adoption. Radiol Clin North Am, 2021. 59(6): p. 1075\u20131083.https:\/\/doi.org\/10.1016\/j.rcl.2021.07.007","journal-title":"Radiol Clin North Am"},{"issue":"1","key":"5144_CR57","doi-asserted-by":"publisher","first-page":"E","DOI":"10.1148\/radiol.229021","volume":"305","author":"D Daye","year":"2022","unstructured":"Daye, D., et al., Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How? Radiology, 2022. 305(1): p. Ehttps:\/\/doi.org\/10.1148\/radiol.229021","journal-title":"Radiology"},{"key":"5144_CR58","doi-asserted-by":"publisher","first-page":"111462","DOI":"10.1016\/j.ejrad.2024.111462","volume":"175","author":"T Martin-Noguerol","year":"2024","unstructured":"Martin-Noguerol, T., P. Lopez-Ubeda, and A. Luna, AI in radiology: Legal responsibilities and the car paradox. Eur J Radiol, 2024. 175: p. 111462https:\/\/doi.org\/10.1016\/j.ejrad.2024.111462","journal-title":"Eur J Radiol"},{"key":"5144_CR59","doi-asserted-by":"publisher","first-page":"1305756","DOI":"10.3389\/fmed.2023.1305756","volume":"10","author":"C Cestonaro","year":"2023","unstructured":"Cestonaro, C., et al., Defining medical liability when artificial intelligence is applied on diagnostic algorithms: a systematic review. Front Med (Lausanne), 2023. 10: p. 1305756https:\/\/doi.org\/10.3389\/fmed.2023.1305756","journal-title":"Front Med (Lausanne)"}],"container-title":["Abdominal Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00261-025-05144-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00261-025-05144-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00261-025-05144-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T03:01:08Z","timestamp":1773111668000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00261-025-05144-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,28]]},"references-count":59,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["5144"],"URL":"https:\/\/doi.org\/10.1007\/s00261-025-05144-y","relation":{},"ISSN":["2366-0058"],"issn-type":[{"value":"2366-0058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,28]]},"assertion":[{"value":"16 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"NL- Editor-in-Chief of Abdominal RadiologyPG: Associate Editors of AI section of Abdominal Radiology.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}