Radiology-Artificial Intelligence最新文献

{"title":"Bridging Industry and Academia: Proceedings from the 2025 Academy Roundtable on AI Implementation in Medical Imaging.","authors":"Alexander J Towbin, Woojin Kim, Nina Kottler","doi":"10.1148/ryai.250671","DOIUrl":"https://doi.org/10.1148/ryai.250671","url":null,"abstract":"<p><p>Despite rapid advancements in artificial intelligence (AI) for medical imaging, widespread clinical adoption remains limited. In March 2025, the Academy for Radiology & Biomedical Imaging Research convened a cross-sector roundtable to examine operational and structural challenges in AI development and implementation. Researchers, department leaders, government representatives, and industry executives participated in a structured two-stage discussion using the AI lifecycle and a simplified failure modes and effects analysis (sFMEA) framework. In the first stage, attendees examined each phase of the AI lifecycle to identify domains where implementation barriers arise. In the second stage, mixed stakeholder groups applied a qualitative sFMEA approach to analyze process vulnerabilities within those domains and discuss mitigation approaches. This manuscript summarizes the session design, synthesizes key domains, and presents illustrative mitigation approaches across five areas: governance, use cases, implementation, cost, and regulation. The discussion identified recurring challenges related to fragmented priorities, infrastructure constraints, and regulatory complexity, as well as the need for clearer governance structures and more consistent evaluation processes to improve coordination across stakeholders.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250671"},"PeriodicalIF":13.2,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Delineation of Couinaud Segments on CT for Future Liver Remnant Volumetry.","authors":"Tejas Sudharshan Mathai, Praveen T S Balamuralikrishna, Vivek Batheja, Xinya Wang, Cathleen Hannah, Michael Kassin, Ifechi Ukeh, Christopher Koh, David E Kleiner, Jonathan M Hernandez, Meghan G Lubner, Perry J Pickhardt, Ronald M Summers","doi":"10.1148/ryai.250808","DOIUrl":"https://doi.org/10.1148/ryai.250808","url":null,"abstract":"<p><p>Purpose To develop a deep learning model that automatically delineates the eight liver Couinaud segments and the spleen on CT for future liver remnant (FLR) volumetry. Materials and Methods In this retrospective study (January 2001 and October 2025), eight liver Couinaud segments and the spleen were manually labeled on CT scans of patients from Institution-A and the public Medical Segmentation Decathlon dataset. A 3D nnU-Net segmentation model was trained on this dataset and evaluated on three datasets (one internal and two external). Results The training dataset included 498 patients (442 from the public Medical Segmentation Decathlon dataset and 56 from Institution-A, mean age 55 ± 7 [SD] years, 38 males), while the testing dataset included 64 patients from Institution-A (50 had liver fibrosis and 8 underwent portal vein embolization; PVE), 197 patients from the publicly available colorectal liver metastases (CRLM) dataset (mean age 59 ± 12 years, 117 males), and 50 patients (25 were healthy and 25 had cirrhosis) from an external Institution-B (mean age 49 ± 9 years, 29 males). For the whole liver in Institution-A and Institution-B, Dice scores of 0.98 ± 0.02 (95% CI: 0.97, 0.99) and 0.98 ± 0.03 (95% CI: 0.97, 0.99), and 95% percentile Hausdorff Distance (HD) errors of 2.5 ± 3.8 mm (95% CI: 1.6, 3.3) and 3.3 ± 6.6 mm (95% CI: 1.4, 5.2) were obtained, respectively. The pre-PVE <i>FLR</i>_% and post-PVE <i>FLR</i>_% volume differences (manual vs automated, 8 patients) were 0.03 ± 2.4 and-0.39 ± 3.0, respectively. For the FLR in the CRLM dataset, a Dice score of 0.99 ± 0.01 (95% CI: 0.99, 0.993) and an HD error of 0.9 ± 1.8 mm (95% CI: 0.6, 1.1) were achieved. Conclusion The model accurately estimated preoperative FLR volumetry and generalized well to patients with colorectal liver metastases, fibrosis, cirrhosis and healthy controls. ©RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250808"},"PeriodicalIF":13.2,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LLM Label Noise and the Established Framework of Imperfect Reference Standard Bias.","authors":"Jeong Hyun Lee, Jaeseung Shin","doi":"10.1148/ryai.260153","DOIUrl":"https://doi.org/10.1148/ryai.260153","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 3","pages":"e260153"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer-based Fusion of Longitudinal Multimodal Radiomic Features from Chest Radiography and CT in COVID-19.","authors":"Chunrui Zou, Walter C Mankowski, Lauren Pantalone, Hannah Horng, Shefali Setia Verma, Eduardo J Mortani Barbosa, Tessa S Cook, Peter B Noel, Erica L Carpenter, Jeffrey C Thompson, Russell T Shinohara, Leonid Roshkovan, Sharyn I Katz, Despina Kontos","doi":"10.1148/ryai.240218","DOIUrl":"10.1148/ryai.240218","url":null,"abstract":"<p><p>Purpose To evaluate the feasibility of a transformer structure for fusing longitudinal multimodal radiomic features from chest radiographs (CXRs) and CT images to predict outcomes and identify associated clinical events in patients with COVID-19. Materials and Methods This retrospective study analyzed de-identified longitudinal CXRs and CT images in patients with polymerase chain reaction-confirmed COVID-19. Proprietary patient data (site 1) were collected between July 2020 and May 2021, and open-access patient data (obtained before February 1, 2020) were collected from site 2. Clinical outcomes included mortality, intensive care unit admission, and ventilator use during any follow-up visit. Radiomic features were extracted from lung regions in CXRs and CT images using the Cancer Imaging Phenomics Toolkit and integrated using a transformer-based model. Patient data were partitioned into training, validation, and test sets (ratio, 65:15:20). Subgroup analyses were performed across sex, site, and modality. Model performance was assessed using area under the receiver operating characteristic curve (AUC) and weighted AUC scores, with statistical significance assessed using Student <i>t</i> tests. Results The study included 2274 patients (946 from site 1, 1328 from site 2; mean age, 59.84 years ± 16.84, 1171 male patients). Weighted testing AUCs for predicting outcomes were 0.86 (95% CI: 0.85, 0.86) for mortality, 0.82 (95% CI: 0.81, 0.82) for intensive care unit admission, and 0.86 (95% CI: 0.86, 0.87) for ventilator usage, outperforming models trained solely on cross-sectional data or single-modal data (<i>P</i> < .05). Conclusion Transformer-based fusion of longitudinal multimodal radiomic data effectively predicted clinical outcomes and events associated with COVID-19. <b>Keywords:</b> COVID-19, Lung, Radiomics, Multi-Head Attention, Multimodal, Longitudinal, CT, Chest Radiography <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240218"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147475349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiopathomic Graph Deep Learning for Multiscale Spatial-Contextual Modeling of Intratumoral Heterogeneity to Predict Breast Cancer Response to Neoadjuvant Therapy.","authors":"Liang-Qin Zhou, Xin-Yi Wang, Ye Xu, Hong-Xia Zhang, Xin-Xin Yang, Rui-Qi Jin, Xi-Qiao Sang, Yue-Min Zhu, Hong-Xue Meng, Zi-Xiang Kuai","doi":"10.1148/ryai.250760","DOIUrl":"10.1148/ryai.250760","url":null,"abstract":"<p><p>Purpose To develop an explainable radiopathomic graph deep learning (RPGDL) system for multiscale spatial-contextual modeling of intratumoral heterogeneity and evaluate its performance for the prediction of pathologic complete response (pCR) to neoadjuvant therapy in breast cancer. Materials and Methods The RPGDL system was developed from dual-center retrospective analysis of patients with biopsy-proven invasive breast cancer (May 2018-August 2024). For each tumor, individual radiomic and pathomic graphs were generated from pretherapeutic MRI and hematoxylin-eosin-stained biopsy slide images, respectively. These graphs were then processed by three distinct graph neural networks (GNNs): radiomic, pathomic, and radiopathomic. GNN performance was assessed with the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). A multifaceted approach was used to explain the GNNs' predictions. Results The training set included 582 (mean age, 52 years ± 9 [SD]) patients and the external test set 468 (50 years ± 10) patients from centers 1 and 2, respectively. The radiomic GNN achieved AUCs of 0.89 (95% CI: 0.85, 0.93) in the training set and 0.84 (95% CI: 0.80, 0.89) in the external test set; the pathomic GNN achieved AUCs of 0.87 (95% CI: 0.83, 0.91) in the training set and 0.83 (95% CI: 0.78, 0.88) in the external test set, with no significant difference between them (<i>P</i> > .05). The radiopathomic GNN outperformed both single-modality GNNs (training set: AUC, 0.95 [95% CI: 0.92, 0.98]; external test set: AUC, 0.91 [95% CI: 0.87, 0.94]; <i>P</i> < .05; NRI and IDI confirmed). Pathomic graphs dominated probability increases for pCR predictions, while radiomic graphs drove probability decreases for non-pCR predictions. Multifaceted analyses verified GNNs' explainability. Conclusion The developed RPGDL system enabled multiscale spatial-contextual intratumoral heterogeneity modeling for high-performance, explainable prediction of pCR to neoadjuvant therapy in breast cancer. <b>Keywords:</b> Dynamic Contrast-enhanced MRI, Breast, Tumor Response, Radiology-Pathology Integration, Prognosis, Principal Component Analysis, Perception, Supervised Learning, Reconstruction Algorithm <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250760"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metrics for Artificial Intelligence in Medicine: A Reference Resource.","authors":"Ricardo A Gonzales, Marcelo Straus Takahashi, Tara Retson, Imon Banerjee, Seong Ho Park, Charles E Kahn","doi":"10.1148/ryai.260070","DOIUrl":"10.1148/ryai.260070","url":null,"abstract":"<p><p>The effective integration of artificial intelligence (AI) systems into clinical medicine depends on comprehensive and transparent performance evaluation; however, the lack of standardized and widely accepted metrics poses challenges for reproducibility and model adoption. A comprehensive, machine-interpretable framework is presented to formalize the nomenclature and descriptions of 207 graphical, matrix, and scalar metrics used to measure AI model performance. The metrics taxonomy, developed as part of the Radiology Ontology of AI Datasets, Models and Projects (ROADMAP), provides a logically structured representation that captures the semantics of AI evaluation metrics, supports reasoning over metric classes, and enables automated completeness checks for AI model reporting. For each metric, the taxonomy incorporates a definition and citations to authoritative reference sources; where applicable, the taxonomy also includes synonyms, abbreviations, alternate language forms, mathematical formulae, and numerical bounds. The taxonomy supports evaluation of models operating on structured data, medical images, audio signals, and/or unstructured text. Logical axioms link each metric to one or more of 18 AI model performance criteria, including classification, calibration, image segmentation, and text analysis. By harmonizing terminology and enabling structured queries, ROADMAP's taxonomy of AI performance metrics facilitates model comparison, bias detection, and selection of appropriate evaluation methods across diverse datasets and clinical tasks. <i>Supplemental material is available for this article.</i> © RSNA, 2026 See also special report on ROADMAP ontology.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e260070"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147436225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commercial AI Model Diagnostic Accuracy for Intracranial Large- and Medium-Vessel Occlusion at Emergency CT Angiography.","authors":"Henrik Andersson, Björn Hansen, Johan Wassélius","doi":"10.1148/ryai.250749","DOIUrl":"10.1148/ryai.250749","url":null,"abstract":"<p><p>The diagnostic accuracy of AIDOC-VO, the first commercial artificial intelligence (AI) tool for intracranial large- and medium-vessel occlusion (LVO and MeVO) detection at head and neck CT angiography (CTA), was evaluated in a multicenter emergency setting. A prospective diagnostic-accuracy study of 3031 adult CTA examinations (mean age ± SD, 67.3 years ± 16.4; 1549 females) acquired March-July 2024 across a 10-hospital region was performed. The AI model was compared with clinical radiology reporting. Examinations flagged positive or doubt by either the AI model or report underwent blinded rereading for reference-standard establishment. Of 3031 CTA examinations, valid AI model output was yielded for 2804 (92.5%), of which 224 of 2804 (8.0%) had vessel occlusion (VO) on reference-standard reading. For VO detection within intended use (218 of 224), sensitivity was 81.7% (178 of 218) (clinical report: 81.2% [177 of 218]; <i>P</i> = .91), and specificity was 99.6% (2569 of 2580) (clinical report: 99.3% [2561 of 2580]; <i>P</i> = .12). LVO sensitivity was 92.8% (64 of 69) (clinical report: 87.0% [60 of 69]; <i>P</i> = .42) and MeVO sensitivity was 76.1% (121 of 159) (clinical report: 79.2% [126 of 159]; <i>P</i> = .55). The AI model identified VOs missed by radiologists in 42 examinations, for an enhanced detection rate of 18.8% (42 of 224; 15 per 1000 CT angiograms), and generated 11 false alerts (3.9 per 1000 CT angiograms). Performance did not differ significantly from clinical radiology reporting. <b>Keywords:</b> CT, CT-Angiography, CNS, Ischemia/Infarction, Stroke, Diagnosis, Classification, Application Domain, Arteries, Artificial Intelligence, Large Vessel Occlusion, Medium Vessel Occlusion <i>Supplemental material is available for this article.</i> © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250749"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147692616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the Age Gap: Longitudinal Aging Velocity as a Dynamic Biomarker in Chest Radiography.","authors":"Paul S Babyn","doi":"10.1148/ryai.260358","DOIUrl":"https://doi.org/10.1148/ryai.260358","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 3","pages":"e260358"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Longitudinal DCE MRI Vascular Textures: Radiologic and Biologic Insights for pCR Prediction in HER2-Negative Breast Cancer.","authors":"Xinzhi Teng, Junjie Ma, Jiang Zhang, Miaoqing Zhao, Xiangjiao Meng, Yong Yin, Haonan Xiao, Qingpei Lai, Xinyu Zhang, Yufeng Jiang, Jing Cai","doi":"10.1148/ryai.250734","DOIUrl":"10.1148/ryai.250734","url":null,"abstract":"<p><p>Purpose To develop a pathologic complete response (pCR) prediction model for human epidermal growth factor receptor 2 (HER2)-negative breast cancer by analyzing longitudinal changes in dynamic contrast-enhanced MRI (DCE MRI)-derived vascular textures. Materials and Methods Retrospective baseline and midtreatment DCE MRI data from I-SPY2 (May 2010-November 2016) and ACRIN 6698 (August 2012-January 2015) trials were used for development and internal tests (ClinicalTrials.gov no. NCT01042379). An independent hospital cohort (December 2023-December 2024) served as the external test. Image Biomarker Standardization Initiative-standardized vascular textures were extracted from the functional tumor volume (FTV). The DCE MRI vascularization-based response tracking (DCE-VASC-TRACK) model incorporated repeatable vascular texture changes associated with pCR at surgery, alongside hormone receptor status, age, baseline FTV, and midtreatment FTV change. Performance was evaluated using the area under the receiver operating curve (AUC). Biologic associations were explored using gene set enrichment analysis. Results The study included 417 (development), 162 (internal test), and 167 (external test) women (mean ± SD ages: 49 years ± 10, 48 years ± 10, 48 years ± 10, respectively). Changes in two features-complexity and run-length variance-were significantly associated with pCR (adjusted odds ratios per SD increase: 2.13 [95% CI: 1.75, 2.63] and 2.34 [95% CI: 1.87, 2.92]; <i>P</i> < .001). In the external test cohort, DCE-VASC-TRACK outperformed the FTV-based model (AUC, 0.86 [95% CI: 0.79, 0.92] vs 0.72 [95% CI: 0.62, 0.79]; <i>P</i> < .001). Vascular textures showed enrichment in angiogenesis, protein secretion, and transforming growth factor-β signaling pathways compared with clinical factors. Conclusion Incorporating DCE MRI vascular texture dynamics at midtreatment significantly improved pCR prediction compared with clinical and functional tumor volume features alone. <b>Keywords:</b> Breast Cancer, Molecular Imaging, Dynamic Contrast-enhanced MRI, Radiogenomics <i>Supplemental material is available for this article.</i> © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license. Clinical trial registration no. NCT01042379 See also commentary by Schnitzler in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250734"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147515356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognitively Biased Prompt Effects on Large Language Model Accuracy for Radiology Board-style Examination Questions.","authors":"Nicholas T Dietrich, Dhruv Patel, Joseph Bellissimo, Christopher T Loh, Pascal N Tyrrell","doi":"10.1148/ryai.250585","DOIUrl":"10.1148/ryai.250585","url":null,"abstract":"<p><p>Large language models (LLMs) are increasingly explored for radiology-related applications, yet their vulnerability to cognitive biases remains undercharacterized. The aim of this study was to investigate whether targeted prompts exploiting cognitive biases degrade LLM accuracy on radiology board-style questions. Ten contemporary LLMs were evaluated on 200 text-based and 200 multimodal American Board of Radiology examination-style questions under baseline and three cognitive bias prompts: authority bias prompts (ABPs), complexity bias prompts (CBPs), and anchoring bias prompts (AnBPs). Two mitigation approaches-a prompt bias audit and a one-shot mitigation strategy-were also evaluated. Under baseline prompts, models achieved a mean accuracy ± SD of 84.8% ± 5.5 (154-186 of 200) for text-based and 59.5% ± 7.7 (101-143 of 200) for multimodal questions. All models showed reduced accuracy to cognitively biased prompts, with ABP, CBP, and AnBP yielding absolute declines of 21.1%, 10.1%, and 4.4%, respectively, for text questions (<i>P</i> < .001 for each), and 44.9%, 44.4%, and 39.6%, respectively, for multimodal questions (<i>P</i> < .001 for each). The prompt bias audit increased accuracy by 5.6% for text-based and 15.8% for multimodal questions, whereas the one-shot mitigation yielded gains of 4.0% for text questions and 24.9% for multimodal questions. These findings demonstrate that LLMs are susceptible to cognitively biased inputs. <b>Keywords:</b> Technology Assessment, Use of AI in Education, Social Implications <i>Supplemental material is available for this article.</i> © RSNA, 2026 See also commentary by Tayebi Arasteh and Truhn in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250585"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147692551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}