BJR artificial intelligence最新文献

{"title":"Advancements in artificial intelligence applications for liver ultrasound imaging.","authors":"Yu-Ting Shen, Hui-Xiong Xu, Bo-Yang Zhou","doi":"10.1093/bjrai/ubaf019","DOIUrl":"10.1093/bjrai/ubaf019","url":null,"abstract":"<p><p>Liver diseases consistently plague people's daily lives as a result of their high morbidity and mortality rates. Ultrasound (US), favoured by its flexibility, free of radiation, cost-effectiveness, and real-time capabilities, has been commonly employed as one of the first-line imaging tools for hepatic conditions. Artificial intelligence (AI) algorithms are increasingly applied to automatically identify intricate patterns and perform quantitative analyses in US imaging, potentially reducing radiologists' workload and improving diagnostic efficiency. AI-based US has been of substantial assistance in detecting, diagnosing, screening as well as monitoring of various liver diseases, and has attracted extensive attention among the medical community. In this review, we present a general introduction to AI in medical imaging; we next review its rapidly evolving applications in liver US, covering evaluation of hepatic steatosis severity, assessment of liver fibrosis, identification of focal hepatic lesions, preoperative prediction of high-risk pathological characteristics, assessment of postoperative prognosis, and the analysis of the model of integrated application of multi-omics data; finally, we present an outlook on the clinical applications of AI-based US in the liver diseases.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"3 1","pages":"ubaf019"},"PeriodicalIF":0.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13222679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148146000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-based deep learning prediction of complete response in intermediate-stage hepatocellular carcinoma treated with drug-eluting beads transarterial chemoembolization.","authors":"Jérémy Dana, Armine Vardazaryan, Benoit Gallix, Maxime Ronot, Jean-Paul Mazellier, Marlee Parsons, Valérie Vilgrain, Thomas F Baumert, Caroline Reinhold, Nicolas Padoy, Jules Grégory","doi":"10.1093/bjrai/ubaf018","DOIUrl":"10.1093/bjrai/ubaf018","url":null,"abstract":"<p><strong>Objectives: </strong>To develop a CT-based deep learning (DL) model to predict complete response (CR) to drug-eluting beads-transarterial chemoembolization (TACE) in patients with naive Barcelona Clinic Liver Cancer (BCLC) B hepatocellular carcinoma (HCC).</p><p><strong>Methods: </strong>This dual-centre retrospective study included 93 patients with BCLC B HCC treated with drug-eluting beads-TACE (median size of 40 mm and 37 mm at Institutions 1 and 2). Complete response was defined as per modified Response Evaluation Criteria in Solid Tumours on liver contrast-enhanced CT within 2 months of treatment. A twin-network DL model with spatio-temporal Video Vision Transformer (ViViT) architecture was developed to predict CR using baseline dedicated liver CT. The model was extensively trained/tested based on an 8-fold cross-validation approach with an ensemble technique, a model vote system where the outcome is the average of multiple model predictions.</p><p><strong>Results: </strong>The CR rate was 36% (18/50) and 22% (11/49) at Institutions 1 and 2. The model showed high specificity and AUC, as well as moderate sensitivity and balanced accuracy when using either the late arterial phase (0.91 ± 0.12, 0.86 ± 0.16, 0.43 ± 0.23, and 0.67 ± 0.13, respectively) or the portal venous phase (0.90 ± 0.15, 0.85 ± 0.17, 0.57 ± 0.30, and 0.74 ± 0.16, respectively).</p><p><strong>Conclusion: </strong>The developed CT-based DL model predicted CR to drug-eluting beads-TACE in patients with naive BCLC B HCC with high specificity. It should be refined to improve sensitivity.</p><p><strong>Advances in knowledge: </strong>The study underscores the potential of artificial intelligence in precision medicine in patients with HCC. While the model shows promise, further research with larger datasets and prospective studies is needed to enhance its predictive power and clinical applicability.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf018"},"PeriodicalIF":0.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic accuracy of artificial intelligence-assisted radiology assessment of cancer: a systematic review.","authors":"Dylan Zhao, Thomas Packer, Xiaobo Jie, Muhammad Shahid, Jason Oke, Annette Plüddemann","doi":"10.1093/bjrai/ubaf016","DOIUrl":"10.1093/bjrai/ubaf016","url":null,"abstract":"<p><strong>Objective: </strong>Perform a systematic review and meta-analysis of studies using multi-reader multi-case (MRMC) study designs for cancer diagnosis with artificial intelligence (AI). Review diagnostic accuracy, study design and reporting.</p><p><strong>Methods: </strong>A search of several databases between January 1, 2014 and February 28, 2024 was performed. Diagnostic accuracy studies that compared radiologists with and without AI-assistance in cancer diagnostic tasks over all imaging modalities were included. Meta-analysis using Summary Receiver Operating Characteristics (SROC) curves were plotted for pooled sensitivity and specificity. Risk of bias was assessed by using the Quality Assessment of Diagnostic Accuracy Studies-Comparative (QUADAS-C) and the Checklist for Artificial intelligence in Medical Imaging (CLAIM).</p><p><strong>Results: </strong>Thirty-four studies were included of which 23 were included in meta-analysis. Eight identified cancers on Chest X-rays, 17 on CT, 9 on MRI. Pooled sensitivity and specificity were 0.67 (95%CI 0.58-0.74) and 0.82 (95%CI 0.75-0.88), respectively, for clinicians and 0.79 (95%CI 0.71-0.88) and 0.87 (95%CI 0.82-0.91) for AI-assistance. 17 of 34 studies (50%) had concern of bias with QUADAS-C. CLAIM assessment highlighted reporting issues in several domains of methodology in a proportion of studies.</p><p><strong>Conclusion: </strong>Artificial intelligence assistance tools may benefit clinician diagnostic performance in cancer diagnosis. Updated reporting guidelines may help to overcome potential methodological limitations to clarify AI's value in healthcare.</p><p><strong>Advances in knowledge: </strong>Previous reviews compare AI accuracy alone against a clinician. We focus on MRMC study designs to ass AI use in a clinical environment.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf016"},"PeriodicalIF":0.0,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lessons learned from a multi-centre implementation of an artificial intelligence algorithm to detect vertebral fractures for radiology, information technology, information governance and clinical leads.","authors":"Rohit Vijjhalwar, Kaiyang Song, David D G Chappell, Jane Turton, Jack Boylan, Jane Threlkeld, Mike Page, Madeleine Sampson, Yuriy Arlachov, Cass Chisholm, Rachel Eckert, Raz Edwards, Amrita Kumar, Elizabeth Curtis, Kenneth E S Poole, Yotam Kimmel, Oren Shalem, Michael Stone, Rafael Pinedo-Villanueva, Opinder Sahota, Muhammad Kassim Javaid","doi":"10.1093/bjrai/ubaf017","DOIUrl":"10.1093/bjrai/ubaf017","url":null,"abstract":"<p><p>Artificial intelligence (AI) algorithms have been developed to identify vertebral fractures through reanalysis of existing CT scans. This study describes a real-world case study of the deployment of an AI solution in the NHS, from information governance (IG) and technology (IT) perspectives, to inform best practice recommendations. Five NHS hospitals were selected to deploy the Nanox AI solution to identify vertebral fractures from existing CT images. The journey to IG and IT assurance was described and used to inform recommendations. The time from contract signing to IG assurance ranged between 5 and 13 months. The period from IG assurance to the analysis of the first patient scan ranged from 7 to 12 months, excluding 1 site withdrawing from the process. Each site required different IG documents: Data Protection Impact Assessment (5/5 sites), Data Protection Agreement (2/5 sites), Digital Technology Assessment Criteria (2/5 sites). IT implementation delays included third-party supplier coordination, NHS IT staff availability, and local capability. Based on the observed challenges, 6 best practice recommendations are proposed to address current challenges to AI adoption in radiology settings in the NHS to support IG and 8 to support IT implementation services. Significant challenges remain if AI is to be routinely used to identify vertebral fractures. The proposed recommendations provide a pathway to improve effective and efficient AI deployment. This study proposes recommendations from IG and IT perspectives to improve the local deployment of AI in the NHS.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf017"},"PeriodicalIF":0.0,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing data scarcity in CT imaging: novel approaches for the detection of radiation-induced pneumonitis.","authors":"Sotiris Raptis, Christos Ilioudis, Kiki Theodorou","doi":"10.1093/bjrai/ubaf014","DOIUrl":"10.1093/bjrai/ubaf014","url":null,"abstract":"<p><strong>Objectives: </strong>Radiation-induced pneumonitis (RP) is a critical complication of radiotherapy in lung cancer patients, and its early detection remains a challenge due to the limited availability of annotated CT imaging data and the subtle nature of disease evolution. The objective of this study is to enhance the detection and localization of RP in CT by integrating advanced data augmentation, self-supervised learning, and synthetic data generation techniques.</p><p><strong>Methods: </strong>A conditional Generative Adversarial Network (cGAN) was used to create synthetic RP images conditioned on lung segmentation masks to create anatomically plausible data for augmenting the training sets. The pipeline was created to possess double-stage self-supervised training with hierarchical pretext tasks to achieve robust features.</p><p><strong>Results: </strong>The performance of the proposed framework, for a 5-fold cross-validation, has an average accuracy of 94.04%, precision of 92.06%, with a recall of 95.1%, an F1-score of 93.56%, and area under the curve of 95.94%.</p><p><strong>Conclusions: </strong>The model was demonstrated to possess superior performance and stability in RP detection and localization, which suggests potential clinical translation.</p><p><strong>Advances in knowledge: </strong>The paper offers a novel fusion of cGAN-generated synthetic data, spatial attention, and contrastive learning to address RP detection in limited data. Interpretability is achieved by introducing Bayesian uncertainty estimation to provide translational value in clinical practice.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf014"},"PeriodicalIF":0.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"General artificial intelligence for the diagnosis and treatment of cancer: the rise of foundation models.","authors":"Ali A Tarhini, Palak Dave, Issam El Naqa","doi":"10.1093/bjrai/ubaf015","DOIUrl":"10.1093/bjrai/ubaf015","url":null,"abstract":"<p><p>Artificial intelligence (AI) and its computer algorithms, including those of machine learning and deep learning, have been transforming the oncology field. Generative AI (Gen AI) and general AI technologies, which can be adapted to various tasks based on diverse inputs, are being increasingly utilized in healthcare applications. Foundation models, driven by Gen AI and general AI technologies, have enthralled the medical world, representing the next generation of large-scale AI tools in medicine that are trained on massive amounts of data and will make a major impact due to their versatility, high performance, personalization enhancement, and assistance of healthcare workers. That said, it is undeniable that there are multiple obstacles that these algorithms must overcome to be successfully utilized in the field. In this review, we first present a brief background and a history of the recent rise of Gen AI and foundation models. Next, we explore their applications in the diagnosis and treatment of cancer, specifically in radiology, pathology, precision medicine, personalization of care, and surgical oncology. Then, we discuss some of the limitations that could hinder general and Gen AI's clinical translation.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf015"},"PeriodicalIF":0.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12478997/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning automates Cobb angle measurement compared with multi-expert observers.","authors":"Keyu Li, Hanxue Gu, Roy Colglazier, Robert Lark, Elizabeth Hubbard, Robert French, Denise Smith, Jikai Zhang, Erin McCrum, Anthony Catanzano, Joseph Cao, Leah Waldman, Maciej A Mazurowski, Benjamin Alman","doi":"10.1093/bjrai/ubaf009","DOIUrl":"10.1093/bjrai/ubaf009","url":null,"abstract":"<p><strong>Objectives: </strong>Scoliosis, a prevalent condition characterized by abnormal spinal curvature leading to deformity, requires precise assessment methods for effective diagnosis and management. The Cobb angle is a widely used scoliosis quantification method that measures the degree of curvature between the tilted vertebrae. Yet, manual measuring of Cobb angles is time-consuming and labour-intensive, fraught with significant interobserver and intraobserver variability. To address these challenges and the lack of interpretability found in certain existing automated methods, we have created fully automated software that not only precisely measures the Cobb angle but also provides clear visualizations of these measurements.</p><p><strong>Methods: </strong>This software integrates a deep neural network-based spine region detection and segmentation, spine centreline identification, pinpointing the most significantly tilted vertebrae, and direct visualization of Cobb angles on the original images.</p><p><strong>Results: </strong>Upon comparison with the assessments of 7 expert readers, our algorithm exhibited a mean deviation in Cobb angle measurements of 4.17 degrees, notably surpassing the manual approach's average intra-reader discrepancy of 5.16 degrees. The algorithm also achieved intraclass correlation coefficients (ICC) exceeding 0.96 and Pearson correlation coefficients above 0.944, reflecting robust agreement with expert assessments and superior measurement reliability.</p><p><strong>Conclusions: </strong>Through the comprehensive reader study and statistical analysis, we believe this algorithm not only ensures a higher consensus with expert readers but also enhances interpretability and reproducibility during assessments. It holds significant promise for clinical application, potentially helping physicians assess and diagnose scoliosis more accurately, thus improving patient care.</p><p><strong>Advances in knowledge: </strong>A fully automated Cobb angle measurement algorithm was compared with a comprehensive multi-expert study and demonstrated superior performance relative to human observers. The code is publicly available at GitHub.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf009"},"PeriodicalIF":0.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating natural language processing into radiation oncology: a practical guide to transformer architecture and large language models.","authors":"Reza Khanmohammadi, Mohammad M Ghassemi, Kyle Verdecchia, Ahmed I Ghanem, Bing Luo, Indrin J Chetty, Hassan Bagher-Ebadian, Farzan Siddiqui, Mohamed Elshaikh, Benjamin Movsas, Kundan Thind","doi":"10.1093/bjrai/ubaf010","DOIUrl":"10.1093/bjrai/ubaf010","url":null,"abstract":"<p><p>Natural language processing (NLP) is a key technique for developing medical artificial intelligence (AI) systems that leverage electronic health record data to build diagnostic and prognostic models. NLP enables the conversion of unstructured clinical text into structured data that can be fed into AI algorithms. The emergence of transformer architecture and large language models (LLMs) has led to advances in NLP for various healthcare tasks, such as entity recognition, relation extraction, sentence similarity, text summarization, and question-answering. In this article, we review the major technical innovations that underpin modern NLP models and present state-of-the-art NLP applications that employ LLMs in radiation oncology research. However, it is crucial to recognize that LLMs are prone to hallucinations, biases, and ethical violations, which necessitate rigorous evaluation and validation prior to clinical deployment. As such, we propose a comprehensive framework for assessing the NLP models based on their purpose and clinical fit, technical performance, bias and trust, legal and ethical implications, and quality assurance prior to implementation in clinical radiation oncology. Our article aims to provide guidance and insights for researchers and clinicians who are interested in developing and using NLP models in clinical radiation oncology.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf010"},"PeriodicalIF":0.0,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"M3: multimodal artificial intelligence for medical report generation and visual question answering from 3D abdominal CT scans.","authors":"Abdullah Hosseini, Ahmed Ibrahim, Ahmed Serag","doi":"10.1093/bjrai/ubaf011","DOIUrl":"10.1093/bjrai/ubaf011","url":null,"abstract":"<p><strong>Objectives: </strong>Medical imaging is indispensable for diagnosis, with abdominal imaging playing a pivotal role in generating medical reports and informing clinical decision-making. Recent works in artificial intelligence (AI), particularly in multimodal approaches such as vision-language models, have demonstrated significant potential to enhance medical image analysis by seamlessly integrating visual and textual data. While 2D imaging has been the main focus of many studies, the enhanced spatial detail and volumetric consistency offered by 3D images, such as CT scans, remain relatively underexplored. This gap underscores the need for innovative approaches to unlock the potential of 3D imaging in clinical workflows.</p><p><strong>Methods: </strong>In this study, we utilized a multimodal AI pipeline, Phi3-V, to address 2 key challenges in abdominal imaging: generating clinically coherent medical reports from 3D CT images and performing visual question answering based on these images.</p><p><strong>Results: </strong>Our optimized model attained an average GREEN score of 0.409 for medical report generation and an accuracy of 79% for multiple-choice visual question answering on the validation cases.</p><p><strong>Conclusions: </strong>These findings demonstrate the potential of multimodal AI in advancing the analysis of 3D medical imaging, paving the way for more robust and efficient applications in healthcare.</p><p><strong>Advances in knowledge: </strong>This study advances the use of multimodal AI for 3D CT imaging, achieving improvements in medical report generation and visual question answering.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf011"},"PeriodicalIF":0.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic modeling in head and neck cancer: deep learning or handcrafted radiomics?","authors":"Varsha Gouthamchand, Louise A F Fonseca, Frank J P Hoebers, Rianne Fijten, Andre Dekker, Leonard Wee, Hannah Mary Thomas T","doi":"10.1093/bjrai/ubaf008","DOIUrl":"10.1093/bjrai/ubaf008","url":null,"abstract":"<p><p>Head and neck squamous cell carcinoma (HNSCC) presents a complex clinical challenge due to its heterogeneous nature and diverse treatment responses. This review critically appraises the performance of handcrafted radiomics (HC) and deep learning (DL) models in prognosticating outcomes in HNSCC patients treated with (chemo)-radiotherapy. The focus was on methodological rigor, performance metrics, and long-term outcome reporting. A comprehensive literature search was conducted up to May 2023, identifying 23 eligible studies that met the inclusion criteria. We analyzed methodological variability and predictive performance metrics of both models for outcomes including overall survival, loco-regional recurrence, and distant metastasi. Our findings concluded that DL models exhibited slightly superior performance metrics compared to HC models, particularly in outcome prediction. However, the highest methodological quality was noted predominantly in HC studies. Substantial variability in methodology, outcome definitions, and performance metrics was observed, highlighting the need for standardization. While DL models show potential for improved prognostic performance, the methodological robustness in HC studies underscores their reliability. This emphasizes the necessity for methodological improvements, including pre-registration of protocols and clinical utility assessments, to enhance the reliability and applicability of radiomics-based prognostic models in clinical practice.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf008"},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}