Radiology-Artificial Intelligence最新文献

{"title":"Collaborative Integration of AI and Human Expertise to Improve Detection of Chest Radiograph Abnormalities.","authors":"Akash Awasthi, Ngan Le, Zhigang Deng, Carol C Wu, Hien Van Nguyen","doi":"10.1148/ryai.240277","DOIUrl":"https://doi.org/10.1148/ryai.240277","url":null,"abstract":"<p><p><i>\"Just Accepted\" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To develop a collaborative AI system that integrates eye gaze data and radiology reports to improve diagnostic accuracy in chest radiograph interpretation by identifying and correcting perceptual errors. Materials and Methods This retrospective study utilized public datasets REFLACX and EGD-CXR to develop a collaborative AI solution, named Collaborative Radiology Expert (CoRaX). It employs a large multimodal model to analyze image embeddings, eye gaze data, and radiology reports, aiming to rectify perceptual errors in chest radiology. The proposed system was evaluated using two simulated error datasets featuring random and uncertain alterations of five abnormalities. Evaluation focused on the system's referral-making process, the quality of referrals, and its performance within collaborative diagnostic settings. Results In the random masking-based error dataset, 28.0% (93/332) of abnormalities were altered. The system successfully corrected 21.3% (71/332) of these errors, with 6.6% (22/332) remaining unresolved. The accuracy of the system in identifying the correct regions of interest for missed abnormalities was 63.0% [95% CI: 59.0%, 68.0%], and 85.7% (240/280) of interactions with radiologists were deemed satisfactory, meaning that the system provided diagnostic aid to radiologists. In the uncertainty-masking-based error dataset, 43.9% (146/332) of abnormalities were altered. The system corrected 34.6% (115/332) of these errors, with 9.3% (31/332) unresolved. The accuracy of predicted regions of missed abnormalities for this dataset was 58.0% [95% CI: 55.0%, 62.0%], and 78.4% (233/297) of interactions were satisfactory. Conclusion The CoRaX system can collaborate efficiently with radiologists and address perceptual errors across various abnormalities in chest radiographs. ©RSNA, 2025.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240277"},"PeriodicalIF":8.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144643693","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":"MR-Transformer: A Vision Transformer-based Deep Learning Model for Total Knee Replacement Prediction Using MRI.","authors":"Chaojie Zhang, Shengjia Chen, Ozkan Cigdem, Haresh Rengaraj Rajamohan, Kyunghyun Cho, Richard Kijowski, Cem M Deniz","doi":"10.1148/ryai.240373","DOIUrl":"https://doi.org/10.1148/ryai.240373","url":null,"abstract":"<p><p><i>\"Just Accepted\" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To develop a transformer-based deep learning model-MR-Transformer-that leverages ImageNet pretraining and three-dimensional (3D) spatial correlations to predict the progression of knee osteoarthritis to TKR using MRI. Materials and Methods This retrospective study included 353 case-control matched pairs of coronal intermediate-weighted turbo spin-echo (COR-IW-TSE) and sagittal intermediate-weighted turbo spin-echo with fat suppression (SAG-IW-TSE-FS) knee MRIs from the Osteoarthritis Initiative (OAI) database, with a follow-up period up to 9 years, and 270 case-control matched pairs of coronal short-tau inversion recovery (COR-STIR) and sagittal proton density fat-saturated (SAG-PD-FAT-SAT) knee MRIs from the Multicenter Osteoarthritis Study (MOST) database, with a follow-up period up to 7 years. Performance of the MR-Transformer to predict the progression of knee osteoarthritis was compared with that of existing state-of-the-art deep learning models (TSE-Net, 3DMeT, and MRNet) using sevenfold nested cross-validation across the four MRI tissue sequences. Results MR-Transformer achieved areas under the receiver operating characteristic curves (AUCs) of 0.88 (95% CI: 0.85, 0.91), 0.88 (95% CI: 0.85, 0.90), 0.86 (95% CI: 0.82, 0.89), and 0.84 (95% CI: 0.81, 0.87) for COR-IW-TSE, SAG-IW-TSE-FS, COR-STIR, and SAG-PD-FAT-SAT, respectively. The model achieved a higher AUC than that of 3DMeT for all MRI sequences (<i>P</i> < .001). The model showed the highest sensitivity of 83% (95% CI: 78, 87%) and specificity of 83% (95% CI: 76, 88%) for the COR-IW-TSE MRI sequence. Conclusion Compared with the existing deep learning models, the MR-Transformer exhibited state-of-the-art performance in predicting the progression of knee osteoarthritis to TKR using MRIs. ©RSNA, 2025.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240373"},"PeriodicalIF":8.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144643694","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":"Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airway Disease.","authors":"Di Zhang, Mingyue Zhao, Xiuxiu Zhou, Yiwei Li, Yu Guan, Yi Xia, Jin Zhang, Qi Dai, Jingfeng Zhang, Li Fan, S Kevin Zhou, Shiyuan Liu","doi":"10.1148/ryai.240680","DOIUrl":"https://doi.org/10.1148/ryai.240680","url":null,"abstract":"<p><p><i>\"Just Accepted\" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To develop a deep learning model that uses a single inspiratory chest CT scan to generate parametric response maps (PRM) and predict functional small airway disease (fSAD). Materials and Methods In this retrospective study, predictive and generative deep learning models for PRM using inspiratory chest CT were developed using a model development dataset with fivefold cross-validation, with PRM derived from paired respiratory CT as the reference standard. Voxel-wise metrics, including sensitivity, area under the receiver operating characteristic curve (AUC), and structural similarity, were used to evaluate model performance in predicting PRM and expiratory CT images. The best performing model was tested on three internal test sets and an external test set. Results The model development dataset of 308 patients (median age, 67 years, [IQR: 62-70 years]; 113 female) was divided into the training set (<i>n</i> = 216), the internal validation set (<i>n</i> = 31), and the first internal test set (<i>n</i> = 61). The generative model outperformed the predictive model in detecting fSAD (sensitivity 86.3% vs 38.9%; AUC 0.86 vs 0.70). The generative model performed well in the second internal (AUCs of 0.64, 0.84, 0.97 for emphysema, fSAD and normal lung tissue), the third internal (AUCs of 0.63, 0.83, 0.97), and the external (AUCs of 0.58, 0.85, 0.94) test sets. Notably, the model exhibited exceptional performance in the PRISm group of the fourth internal test set (AUC = 0.62, 0.88, and 0.96). Conclusion The proposed generative model, using a single inspiratory CT, outperformed existing algorithms in PRM evaluation, achieved comparable results to paired respiratory CT. Published under a CC BY 4.0 license.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240680"},"PeriodicalIF":8.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144643706","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":"Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-based Pharmacokinetic Quantification of DCE MRI.","authors":"Chaowei Wu, Lixia Wang, Nan Wang, Stephen Shiao, Tai Dou, Yin-Chen Hsu, Anthony G Christodoulou, Yibin Xie, Debiao Li","doi":"10.1148/ryai.240769","DOIUrl":"10.1148/ryai.240769","url":null,"abstract":"<p><p><i>\"Just Accepted\" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To improve the generalizability of pathologic complete response (pCR) prediction following neoadjuvant chemotherapy using deep learning (DL)-based retrospective pharmacokinetic quantification (RoQ) of early-treatment dynamic contrast-enhanced (DCE) MRI. Materials and Methods This multicenter retrospective study included breast MRI data from four publicly available datasets of patients with breast cancer acquired from May 2002 to November 2016. RoQ was performed using a previously developed DL model for clinical multiphasic DCE-MRI datasets. Radiomic analysis was performed on RoQ maps and conventional enhancement maps. These data, together with clinicopathologic variables and shape-based radiomic analysis, were subsequently applied in pCR prediction using logistic regression. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC). Results A total of 1073 female patients with breast cancer were included. The proposed method showed improved consistency and generalizability compared with the reference method, achieving higher AUCs across external datasets (0.82 [CI: 0.72-0.91], 0.75 [CI: 0.71-0.79], and 0.77 [CI: 0.66-0.86] for Datasets A2, B, and C, respectively). On Dataset A2 (from the same study as the training dataset), there was no significant difference in performance between the proposed method and reference method (<i>P</i> = .80). Notably, on the combined external datasets, the proposed method significantly outperformed the reference method (AUC: 0.75 [CI: 0.72- 0.79] vs 0.71 [CI: 0.68-0.76], <i>P</i> = .003). Conclusion This work offers a novel approach to improve the generalizability and predictive accuracy of pCR response in breast cancer across diverse datasets, achieving higher and more consistent AUC scores than existing methods. ©RSNA, 2025.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240769"},"PeriodicalIF":8.1,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144592462","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":"The Evolution of Radiology Image Annotation in the Era of Large Language Models.","authors":"Adam E Flanders, Xindi Wang, Carol C Wu, Felipe C Kitamura, George Shih, John Mongan, Yifan Peng","doi":"10.1148/ryai.240631","DOIUrl":"10.1148/ryai.240631","url":null,"abstract":"<p><p>Although there are relatively few diverse, high-quality medical imaging datasets on which to train computer vision artificial intelligence models, even fewer datasets contain expertly classified observations that can be repurposed to train or test such models. The traditional annotation process is laborious and time-consuming. Repurposing annotations and consolidating similar types of annotations from disparate sources has never been practical. Until recently, the use of natural language processing to convert a clinical radiology report into labels required custom training of a language model for each use case. Newer technologies such as large language models have made it possible to generate accurate and normalized labels at scale, using only clinical reports and specific prompt engineering. The combination of automatically generated labels extracted and normalized from reports in conjunction with foundational image models provides a means to create labels for model training. This article provides a short history and review of the annotation and labeling process of medical images, from the traditional manual methods to the newest semiautomated methods that provide a more scalable solution for creating useful models more efficiently. <b>Keywords:</b> Feature Detection, Diagnosis, Semi-supervised Learning © RSNA, 2025.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240631"},"PeriodicalIF":8.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048059","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":"Unlocking Robust Segmentation: Decoding Domain Randomization for Radiologists.","authors":"John D Mayfield","doi":"10.1148/ryai.250384","DOIUrl":"https://doi.org/10.1148/ryai.250384","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 4","pages":"e250384"},"PeriodicalIF":8.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144592463","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":"Deep Learning with Domain Randomization in Image and Feature Spaces for Abdominal Multiorgan Segmentation on CT and MRI Scans.","authors":"Yu Shi, Lixia Wang, Touseef Ahmad Qureshi, Zengtian Deng, Yibin Xie, Debiao Li","doi":"10.1148/ryai.240586","DOIUrl":"10.1148/ryai.240586","url":null,"abstract":"<p><p>Purpose To develop a deep learning segmentation model that can segment abdominal organs on CT and MRI scans with high accuracy and generalization ability. Materials and Methods In this study, an extended nnU-Net model was trained for abdominal organ segmentation. A domain randomization method in both the image and feature space was developed to improve the generalization ability under cross-site and cross-modality settings on public prostate MRI and abdominal CT and MRI datasets. The prostate MRI dataset contains data from multiple health care institutions, with domain shifts. The abdominal CT and MRI dataset is structured for cross-modality evaluation: training on one modality (eg, MRI) and testing on the other (eg, CT). This domain randomization method was then used to train a segmentation model with enhanced generalization ability on the abdominal multiorgan segmentation challenge dataset to improve abdominal CT and MR multiorgan segmentation, and the model was compared with two commonly used segmentation algorithms (TotalSegmentator and MRSegmentator). Model performance was evaluated using the Dice similarity coefficient (DSC). Results The proposed domain randomization method showed improved generalization ability on the cross-site and cross-modality datasets compared with the state-of-the-art methods. The segmentation model using this method outperformed two other publicly available segmentation models on data from unseen test domains (mean DSC, 0.88 vs 0.79 [<i>P</i> < .001] and 0.88 vs 0.76 [<i>P</i> < .001]). Conclusion The combination of image and feature domain randomizations improved the accuracy and generalization ability of deep learning-based abdominal segmentation on CT and MR images. <b>Keywords:</b> Segmentation, CT, MR Imaging, Neural Networks, MRI, Domain Randomization <i>Supplemental material is available for this article.</i> © RSNA, 2025 See also commentary by Mayfield in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240586"},"PeriodicalIF":8.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112076","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":"Cybersecurity Threats and Mitigation Strategies for Large Language Models in Health Care.","authors":"Tugba Akinci D'Antonoli, Ali S Tejani, Bardia Khosravi, Christian Bluethgen, Felix Busch, Keno K Bressem, Lisa C Adams, Mana Moassefi, Shahriar Faghani, Judy Wawira Gichoya","doi":"10.1148/ryai.240739","DOIUrl":"10.1148/ryai.240739","url":null,"abstract":"<p><p>The integration of large language models (LLMs) into health care offers tremendous opportunities to improve medical practice and patient care. Besides being susceptible to biases and threats common to all artificial intelligence (AI) systems, LLMs pose unique cybersecurity risks that must be carefully evaluated before these AI models are deployed in health care. LLMs can be exploited in several ways, such as malicious attacks, privacy breaches, and unauthorized manipulation of patient data. Moreover, malicious actors could use LLMs to infer sensitive patient information from training data. Furthermore, manipulated or poisoned data fed into these models could change their results in a way that is beneficial for the malicious actors. This report presents the cybersecurity challenges posed by LLMs in health care and provides strategies for mitigation. By implementing robust security measures and adhering to best practices during the model development, training, and deployment stages, stakeholders can help minimize these risks and protect patient privacy. <b>Keywords:</b> Computer Applications-General (Informatics), Application Domain, Large Language Models, Artificial Intelligence, Cybersecurity © RSNA, 2025.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240739"},"PeriodicalIF":8.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143999117","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":"RadioRAG: Online Retrieval-Augmented Generation for Radiology Question Answering.","authors":"Soroosh Tayebi Arasteh, Mahshad Lotfinia, Keno Bressem, Robert Siepmann, Lisa Adams, Dyke Ferber, Christiane Kuhl, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn","doi":"10.1148/ryai.240476","DOIUrl":"10.1148/ryai.240476","url":null,"abstract":"<p><p>Purpose To evaluate diagnostic accuracy of various large language models (LLMs) when answering radiology-specific questions with and without access to additional online, up-to-date information via retrieval-augmented generation (RAG). Materials and Methods The authors developed radiology RAG (RadioRAG), an end-to-end framework that retrieves data from authoritative radiologic online sources in real-time. RAG incorporates information retrieval from external sources to supplement the initial prompt, grounding the model's response in relevant information. Using 80 questions from the RSNA Case Collection across radiologic subspecialties and 24 additional expert-curated questions with reference standard answers, LLMs (GPT-3.5-turbo [OpenAI], GPT-4, Mistral 7B, Mixtral 8×7B [Mistral], and Llama3-8B and -70B [Meta]) were prompted with and without RadioRAG in a zero-shot inference scenario (temperature ≤ 0.1, top-p = 1). RadioRAG retrieved context-specific information from <i>www.radiopaedia.org</i>. Accuracy of LLMs with and without RadioRAG in answering questions from each dataset was assessed. Statistical analyses were performed using bootstrapping while preserving pairing. Additional assessments included comparison of model with human performance and comparison of time required for conventional versus RadioRAG-powered question answering. Results RadioRAG improved accuracy for some LLMs, including GPT-3.5-turbo (74% [59 of 80] vs 66% [53 of 80], false discovery rate [FDR] = 0.03) and Mixtral 8×7B (76% [61 of 80] vs 65% [52 of 80], FDR = 0.02) on the RSNA radiology question answering (RSNA-RadioQA) dataset, with similar trends in the ExtendedQA dataset. Accuracy exceeded that of a human expert (63% [50 of 80], FDR ≤ 0.007) for these LLMs, although not for Mistral 7B-instruct-v0.2, Llama3-8B, and Llama3-70B (FDR ≥ 0.21). RadioRAG reduced hallucinations for all LLMs (rate, 6%-25%). RadioRAG increased estimated response time fourfold. Conclusion RadioRAG shows potential to improve LLM accuracy and factuality in radiology QA by integrating real-time, domain-specific data. <b>Keywords:</b> Retrieval-augmented Generation, Informatics, Computer-aided Diagnosis, Large Language Models <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240476"},"PeriodicalIF":8.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144327031","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":"Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging.","authors":"Nikolas Koutsoubis, Asim Waqas, Yasin Yilmaz, Ravi P Ramachandran, Matthew B Schabath, Ghulam Rasool","doi":"10.1148/ryai.240637","DOIUrl":"10.1148/ryai.240637","url":null,"abstract":"<p><p>Artificial intelligence (AI) has demonstrated strong potential in automating medical imaging tasks, with potential applications across disease diagnosis, prognosis, treatment planning, and posttreatment surveillance. However, privacy concerns surrounding patient data remain a major barrier to the widespread adoption of AI in clinical practice, because large and diverse training datasets are essential for developing accurate, robust, and generalizable AI models. Federated learning offers a privacy-preserving solution by enabling collaborative model training across institutions without sharing sensitive data. Instead, model parameters, such as model weights, are exchanged between participating sites. Despite its potential, federated learning is still in its early stages of development and faces several challenges. Notably, sensitive information can still be inferred from the shared model parameters. Additionally, postdeployment data distribution shifts can degrade model performance, making uncertainty quantification essential. In federated learning, this task is particularly challenging due to data heterogeneity across participating sites. This review provides a comprehensive overview of federated learning, privacy-preserving federated learning, and uncertainty quantification in federated learning. Key limitations in current methodologies are identified, and future research directions are proposed to enhance data privacy and trustworthiness in medical imaging applications. <b>Keywords:</b> Supervised Learning, Perception, Neural Networks, Radiology-Pathology Integration <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240637"},"PeriodicalIF":8.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053086","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}