BJR artificial intelligence最新文献

{"title":"Large language models in cancer: potentials, risks, and safeguards.","authors":"Md Muntasir Zitu, Tuan Dung Le, Thanh Duong, Shohreh Haddadan, Melany Garcia, Rossybelle Amorrortu, Yayi Zhao, Dana E Rollison, Thanh Thieu","doi":"10.1093/bjrai/ubae019","DOIUrl":"10.1093/bjrai/ubae019","url":null,"abstract":"<p><p>This review examines the use of large language models (LLMs) in cancer, analysing articles sourced from PubMed, Embase, and Ovid Medline, published between 2017 and 2024. Our search strategy included terms related to LLMs, cancer research, risks, safeguards, and ethical issues, focusing on studies that utilized text-based data. 59 articles were included in the review, categorized into 3 segments: quantitative studies on LLMs, chatbot-focused studies, and qualitative discussions on LLMs on cancer. Quantitative studies highlight LLMs' advanced capabilities in natural language processing (NLP), while chatbot-focused articles demonstrate their potential in clinical support and data management. Qualitative research underscores the broader implications of LLMs, including the risks and ethical considerations. Our findings suggest that LLMs, notably ChatGPT, have potential in data analysis, patient interaction, and personalized treatment in cancer care. However, the review identifies critical risks, including data biases and ethical challenges. We emphasize the need for regulatory oversight, targeted model development, and continuous evaluation. In conclusion, integrating LLMs in cancer research offers promising prospects but necessitates a balanced approach focusing on accuracy, ethical integrity, and data privacy. This review underscores the need for further study, encouraging responsible exploration and application of artificial intelligence in oncology.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubae019"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11703354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961093","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":"Foundational artificial intelligence models and modern medical practice.","authors":"Alpay Medetalibeyoglu, Yury S Velichko, Eric M Hart, Ulas Bagci","doi":"10.1093/bjrai/ubae018","DOIUrl":"10.1093/bjrai/ubae018","url":null,"abstract":"<p><p>Our opinion piece pays homage to the evolution of medical practices, tracing back to the era of Hippocrates, through significant historical milestones, and drawing parallels with the principles underpinning foundational artificial intelligence (AI) models. It emphasizes the shared ethos of both domains: a commitment to comprehensive care that values diverse data integration and individualized patient treatment. The excitement surrounding foundation models in medical imaging is understandable. However, a critical and cautious approach is crucial before widespread adoption. By addressing the present 4 major limitations (ie, data bias and generalizability, interpretability of AI models, data scarcity and diversity, and computational resources and infrastructure) and fostering a culture of rigorous research, we can unlock the true potential of these models and revolutionize medical care. This critique (opinion) paper highlights the need for a more measured approach in the field of <i>foundation AI models</i> for medicine in general and for medical imaging in particular. It emphasizes the importance of tackling core challenges before rushing toward clinical applications. By focusing on robust methodologies and addressing limitations, researchers can ensure the development of truly impactful and trustworthy models for the betterment of healthcare.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubae018"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934391","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":"Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward.","authors":"Kanika Bhalla, Qi Xiao, José Marcio Luna, Emily Podany, Tabassum Ahmad, Foluso O Ademuyiwa, Andrew Davis, Debbie Lee Bennett, Aimilia Gastounioti","doi":"10.1093/bjrai/ubae016","DOIUrl":"10.1093/bjrai/ubae016","url":null,"abstract":"<p><p>Breast cancer is one of the most common and deadly cancers in women. Triple-negative breast cancer (TNBC) accounts for approximately 10%-15% of breast cancer diagnoses and is an aggressive molecular breast cancer subtype associated with important challenges in its diagnosis, treatment, and prognostication. This poses an urgent need for developing more effective and personalized imaging biomarkers for TNBC. Towards this direction, artificial intelligence (AI) for radiologic imaging holds a prominent role, leveraging unique advantages of radiologic breast images, being used routinely for TNBC diagnosis, staging, and treatment planning, and offering high-resolution whole-tumour visualization, combined with the immense potential of AI to elucidate anatomical and functional properties of tumours that may not be easily perceived by the human eye. In this review, we synthesize the current state-of-the-art radiologic imaging applications of AI in assisting TNBC diagnosis, treatment, and prognostication. Our goal is to provide a comprehensive overview of radiomic and deep learning-based AI developments and their impact on advancing TNBC management over the last decade (2013-2024). For completeness of the review, we start with a brief introduction of AI, radiomics, and deep learning. Next, we focus on clinically relevant AI-based diagnostic, predictive, and prognostic models for radiologic breast images evaluated in TNBC. We conclude with opportunities and future directions for AI towards advancing diagnosis, treatment response predictions, and prognostic evaluations for TNBC.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"1 1","pages":"ubae016"},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11974408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813391","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":"Clinical adoption of deep learning target auto-segmentation for radiation therapy: challenges, clinical risks, and mitigation strategies.","authors":"Alessia De Biase, Nanna Maria Sijtsema, Tomas Janssen, Coen Hurkmans, Charlotte Brouwer, Peter van Ooijen","doi":"10.1093/bjrai/ubae015","DOIUrl":"10.1093/bjrai/ubae015","url":null,"abstract":"<p><p>Radiation therapy is a localized cancer treatment that relies on precise delineation of the target to be treated and healthy tissues to guarantee optimal treatment effect. This step, known as contouring or segmentation, involves identifying both target volumes and organs at risk on imaging modalities like CT, PET, and MRI to guide radiation delivery. Manual segmentation, however, is time-consuming and highly subjective, despite the presence of contouring guidelines. In recent years, automated segmentation methods, particularly deep learning models, have shown promise in addressing this task. However, challenges persist in their clinical use, including the need for robust quality assurance (QA) processes and addressing clinical risks associated with the use of the models. This review examines the challenges and considerations of the clinical adoption of deep learning target auto-segmentation in radiotherapy, focused on the target volume. We discuss potential clinical risks (eg, over- and under-segmentation, automation bias, and appropriate trust), mitigation strategies (eg, human oversight, uncertainty quantification, and education of clinical professionals), and we highlight the importance of expanding QA to include geometric, dose-volume, and outcome-based performance monitoring. While deep learning target auto-segmentation offers significant potential benefits, careful attention to clinical risks and rigorous QA measures are essential for its successful integration in clinical practice.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"1 1","pages":"ubae015"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825618","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":"Digital pathology and multimodal learning on oncology data.","authors":"Asim Waqas, Javeria Naveed, Warda Shahnawaz, Shoaib Asghar, Marilyn M Bui, Ghulam Rasool","doi":"10.1093/bjrai/ubae014","DOIUrl":"10.1093/bjrai/ubae014","url":null,"abstract":"<p><p>Cancer presents a complex tapestry of biological, clinical, and molecular characteristics that collectively influence its diagnosis, progression, and treatment. This review article delves into the recent advancements in integrating multimodal oncology data, a pivotal aspect in the evolving field of digital pathology (DP). The complexity and heterogeneity of cancer, characterized by its multimodal nature, present significant challenges in diagnosis and treatment. Traditional methods of oncology data analysis often fall short of capturing the comprehensive landscape of cancer's multifaceted characteristics. The advent of artificial intelligence, machine learning, and deep learning has marked a new era in multimodal learning. These technologies have revolutionized how oncologists and researchers understand and approach cancer, allowing for a more nuanced and detailed analysis. In this review article, we attempt to examine and present how DP enriches its methods and analysis with other data modalities, including clinical, radiological, and molecular information. We present opportunities and challenges of multimodal learning in oncology, highlighting the synergistic potential of combining various data types for improving cancer care with a focus on DP. Continuous innovation in multimodal data integration will be instrumental in transforming cancer diagnosis, treatment planning, prognosis, and post-treatment surveillance.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"1 1","pages":"ubae014"},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825246","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":"Diffusion models for medical image reconstruction.","authors":"George Webber, Andrew J Reader","doi":"10.1093/bjrai/ubae013","DOIUrl":"10.1093/bjrai/ubae013","url":null,"abstract":"<p><p>Better algorithms for medical image reconstruction can improve image quality and enable reductions in acquisition time and radiation dose. A prior understanding of the distribution of plausible images is key to realising these benefits. Recently, research into deep-learning image reconstruction has started to look into using unsupervised diffusion models, trained only on high-quality medical images (ie, without needing paired scanner measurement data), for modelling this prior understanding. Image reconstruction algorithms incorporating unsupervised diffusion models have already attained state-of-the-art accuracy for reconstruction tasks ranging from highly accelerated MRI to ultra-sparse-view CT and low-dose PET. Key advantages of diffusion model approach over previous deep learning approaches for reconstruction include state-of-the-art image distribution modelling, improved robustness to domain shift, and principled quantification of reconstruction uncertainty. If hallucination concerns can be alleviated, their key advantages and impressive performance could mean these algorithms are better suited to clinical use than previous deep-learning approaches. In this review, we provide an accessible introduction to image reconstruction and diffusion models, outline guidance for using diffusion-model-based reconstruction methodology, summarise modality-specific challenges, and identify key research themes. We conclude with a discussion of the opportunities and challenges of using diffusion models for medical image reconstruction.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"1 1","pages":"ubae013"},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825025","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":"Clinical applications of generative artificial intelligence in radiology: image translation, synthesis, and text generation.","authors":"Zhiqi Zhong, Xueqian Xie","doi":"10.1093/bjrai/ubae012","DOIUrl":"10.1093/bjrai/ubae012","url":null,"abstract":"<p><p>Generative artificial intelligence (AI) has enabled tasks in radiology, including tools for improving image quality. Recently, new hotspots have emerged, such as intra- or inter-modal image translation, task-specific image synthesis, and text generation. Advances in generative AI have facilitated the move towards low-dose, cost-effective, and high-quality radiological image acquisition. Large language models can aid radiologists by generating professional answers and facilitating patient-physician communications. However, radiologists must be aware of potential inaccuracies in the generated content and should only use such tools after rigorous validation of their performance.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"1 1","pages":"ubae012"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824995","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":"Applications and implementation of generative artificial intelligence in cardiovascular imaging with a focus on ethical and legal considerations: what cardiovascular imagers need to know!","authors":"Ahmed Marey, Kevin Christopher Serdysnki, Benjamin D Killeen, Mathias Unberath, Muhammad Umair","doi":"10.1093/bjrai/ubae008","DOIUrl":"10.1093/bjrai/ubae008","url":null,"abstract":"<p><p>Machine learning (ML) and deep learning (DL) have potential applications in medicine. This overview explores the applications of AI in cardiovascular imaging, focusing on echocardiography, cardiac MRI (CMR), coronary CT angiography (CCTA), and CT morphology and function. AI, particularly DL approaches like convolutional neural networks, enhances standardization in echocardiography. In CMR, undersampling techniques and DL-based reconstruction methods, such as variational neural networks, improve efficiency and accuracy. ML in CCTA aids in diagnosing coronary artery disease, assessing stenosis severity, and analyzing plaque characteristics. Automatic segmentation of cardiac structures and vessels using AI is discussed, along with its potential in congenital heart disease diagnosis and 3D printing applications. Overall, AI integration in cardiovascular imaging shows promise for enhancing diagnostic accuracy and efficiency across modalities. The growing use of Generative Adversarial Networks in cardiovascular imaging brings substantial advancements but raises ethical concerns. The \"black box\" problem in DL models poses challenges for interpretability crucial in clinical practice. Evaluation metrics like ROC curves, image quality, clinical relevance, diversity, and quantitative performance assess GAI models. Automation bias highlights the risk of unquestioned reliance on AI outputs, demanding careful implementation and ethical frameworks. Ethical considerations involve transparency, respect for persons, beneficence, and justice, necessitating standardized evaluation protocols. Health disparities emerge if AI training lacks diversity, impacting diagnostic accuracy. AI language models, like GPT-4, face hallucination issues, posing ethical and legal challenges in healthcare. Regulatory frameworks and ethical governance are crucial for fair and accountable AI. Ongoing research and development are vital to evolving AI ethics.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"1 1","pages":"ubae008"},"PeriodicalIF":0.0,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045692/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825708","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":"AI and machine learning in medical imaging: key points from development to translation.","authors":"Ravi K Samala, Karen Drukker, Amita Shukla-Dave, Heang-Ping Chan, Berkman Sahiner, Nicholas Petrick, Hayit Greenspan, Usman Mahmood, Ronald M Summers, Georgia Tourassi, Thomas M Deserno, Daniele Regge, Janne J Näppi, Hiroyuki Yoshida, Zhimin Huo, Quan Chen, Daniel Vergara, Kenny H Cha, Richard Mazurchuk, Kevin T Grizzard, Henkjan Huisman, Lia Morra, Kenji Suzuki, Samuel G Armato, Lubomir Hadjiiski","doi":"10.1093/bjrai/ubae006","DOIUrl":"10.1093/bjrai/ubae006","url":null,"abstract":"<p><p>Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"1 1","pages":"ubae006"},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11140849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201511","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":"Making head and neck cancer clinical data Findable-Accessible-Interoperable-Reusable to support multi-institutional collaboration and federated learning.","authors":"Varsha Gouthamchand, Ananya Choudhury, Frank J P Hoebers, Frederik W R Wesseling, Mattea Welch, Sejin Kim, Joanna Kazmierska, Andre Dekker, Benjamin Haibe-Kains, Johan van Soest, Leonard Wee","doi":"10.1093/bjrai/ubae005","DOIUrl":"10.1093/bjrai/ubae005","url":null,"abstract":"<p><strong>Objectives: </strong>Federated learning (FL) is a group of methodologies where statistical modelling can be performed without exchanging identifiable patient data between cooperating institutions. To realize its potential for AI development on clinical data, a number of bottlenecks need to be addressed. One of these is making data Findable-Accessible-Interoperable-Reusable (FAIR). The primary aim of this work is to show that tools making data FAIR allow consortia to collaborate on privacy-aware data exploration, data visualization, and training of models on each other's original data.</p><p><strong>Methods: </strong>We propose a \"Schema-on-Read\" FAIR-ification method that adapts for different (re)analyses without needing to change the underlying original data. The procedure involves (1) decoupling the contents of the data from its schema and database structure, (2) annotation with semantic ontologies as a metadata layer, and (3) readout using semantic queries. Open-source tools are given as Docker containers to help local investigators prepare their data on-premises.</p><p><strong>Results: </strong>We created a federated privacy-preserving visualization dashboard for case mix exploration of 5 distributed datasets with no common schema at the point of origin. We demonstrated robust and flexible prognostication model development and validation, linking together different data sources-clinical risk factors and radiomics.</p><p><strong>Conclusions: </strong>Our procedure leads to successful (re)use of data in FL-based consortia without the need to impose a common schema at every point of origin of data.</p><p><strong>Advances in knowledge: </strong>This work supports the adoption of FL within the healthcare AI community by sharing means to make data more FAIR.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"1 1","pages":"ubae005"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825234","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}