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Annual Review of Biomedical Data Science最新文献

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Strategies for Creating Robust Patient Groups to Study Diverse Conditions with Electronic Health Records.
IF 7
Annual Review of Biomedical Data Science Pub Date : 2025-04-08 DOI: 10.1146/annurev-biodatasci-020722-114525
Grace D Ramey, Hannah Takasuka, John A Capra
{"title":"Strategies for Creating Robust Patient Groups to Study Diverse Conditions with Electronic Health Records.","authors":"Grace D Ramey, Hannah Takasuka, John A Capra","doi":"10.1146/annurev-biodatasci-020722-114525","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-020722-114525","url":null,"abstract":"<p><p>The growth of electronic health record (EHR) databases in size and availability has created an unprecedented opportunity to better understand human health and disease. However, conducting robust EHR studies requires careful filtering criteria and study design, as EHRs pose several challenges that can confound analyses and lead to inaccurate results. Here we review these challenges and make suggestions about how to avoid or adjust for major confounders and biases in common EHR study designs. We further highlight qualities of EHR data that make different diseases more or less feasible for study. These recommendations for conducting research using EHRs will help inform database selection, improve reproducibility of results across the field, and enhance the validity of study results.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812613","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}
引用次数: 0
Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare.
IF 7
Annual Review of Biomedical Data Science Pub Date : 2025-04-08 DOI: 10.1146/annurev-biodatasci-103123-094851
Yifan Yang, Qiao Jin, Qingqing Zhu, Zhizheng Wang, Francisco Erramuspe Álvarez, Nicholas Wan, Benjamin Hou, Zhiyong Lu
{"title":"Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare.","authors":"Yifan Yang, Qiao Jin, Qingqing Zhu, Zhizheng Wang, Francisco Erramuspe Álvarez, Nicholas Wan, Benjamin Hou, Zhiyong Lu","doi":"10.1146/annurev-biodatasci-103123-094851","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-103123-094851","url":null,"abstract":"<p><p>Large language models (LLMs) have gained significant attention in the medical domain for their human-level capabilities, leading to increased efforts to explore their potential in various healthcare applications. However, despite such a promising future, there are multiple challenges and obstacles that remain for their real-world uses in practical settings. This work discusses key challenges for LLMs in medical applications from four unique aspects: operational vulnerabilities, ethical and social considerations, performance and assessment difficulties, and legal and regulatory compliance. Addressing these challenges is crucial for leveraging LLMs to their full potential and ensuring their responsible integration into healthcare.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812609","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}
引用次数: 0
Revisiting Technical Bias Mitigation Strategies.
IF 7
Annual Review of Biomedical Data Science Pub Date : 2025-04-08 DOI: 10.1146/annurev-biodatasci-103123-095737
Abdoul Jalil Djiberou Mahamadou, Artem A Trotsyuk
{"title":"Revisiting Technical Bias Mitigation Strategies.","authors":"Abdoul Jalil Djiberou Mahamadou, Artem A Trotsyuk","doi":"10.1146/annurev-biodatasci-103123-095737","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-103123-095737","url":null,"abstract":"<p><p>Efforts to mitigate bias and enhance fairness in the artificial intelligence (AI) community have predominantly focused on technical solutions. While numerous reviews have addressed bias in AI, this review uniquely focuses on the practical limitations of technical solutions in healthcare settings, providing a structured analysis across five key dimensions affecting their real-world implementation: who defines bias and fairness, which mitigation strategy to use and prioritize among dozens that are inconsistent and incompatible, when in the AI development stages the solutions are most effective, for which populations, and the context for which the solutions are designed. We illustrate each limitation with empirical studies focusing on healthcare and biomedical applications. Moreover, we discuss how value-sensitive AI, a framework derived from technology design, can engage stakeholders and ensure that their values are embodied in bias and fairness mitigation solutions. Finally, we discuss areas that require further investigation and provide practical recommendations to address the limitations covered in the study.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812611","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}
引用次数: 0
Integrative Data Science in Drug Safety Research: Experiences, Challenges, and Perspectives.
IF 7
Annual Review of Biomedical Data Science Pub Date : 2025-04-01 DOI: 10.1146/annurev-biodatasci-103123-095506
Ferran Sanz
{"title":"Integrative Data Science in Drug Safety Research: Experiences, Challenges, and Perspectives.","authors":"Ferran Sanz","doi":"10.1146/annurev-biodatasci-103123-095506","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-103123-095506","url":null,"abstract":"<p><p>Pharmaceutical research and development largely depend on the quantity and quality of data that are available to support projects. The secondary use of data by means of collaborative and integrative approaches is yielding promising results in drug safety research. However, there are challenges that must be overcome in these integrative approaches, such as interoperability issues, intellectual property protection, and, in the case of clinical information, personal data safeguards. The OMOP common data model and the EHDEN and DARWIN EU platforms constitute successful examples of data sharing initiatives in the clinical domain, while the eTOX, eTRANSAFE, and VICT3R international projects are examples of corporate data sharing in toxicology research. The VICT3R project is using these shared data for generating virtual control groups to be applied in nonclinical drug safety assessment. Drug-related knowledge bases that integrate information from different sources also constitute useful tools in the drug safety domain.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765289","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}
引用次数: 0
The Development Landscape of Large Language Models for Biomedical Applications.
IF 7
Annual Review of Biomedical Data Science Pub Date : 2025-04-01 DOI: 10.1146/annurev-biodatasci-102224-074736
Zhiyuan Cao, Vipina K Keloth, Qianqian Xie, Lingfei Qian, Yuntian Liu, Yan Wang, Rui Shi, Weipeng Zhou, Gui Yang, Jeffrey Zhang, Xueqing Peng, Ethan Zhen, Ruey-Ling Weng, Qingyu Chen, Hua Xu
{"title":"The Development Landscape of Large Language Models for Biomedical Applications.","authors":"Zhiyuan Cao, Vipina K Keloth, Qianqian Xie, Lingfei Qian, Yuntian Liu, Yan Wang, Rui Shi, Weipeng Zhou, Gui Yang, Jeffrey Zhang, Xueqing Peng, Ethan Zhen, Ruey-Ling Weng, Qingyu Chen, Hua Xu","doi":"10.1146/annurev-biodatasci-102224-074736","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-102224-074736","url":null,"abstract":"<p><p>Large language models (LLMs) have become powerful tools for biomedical applications, offering potential to transform healthcare and medical research. Since the release of ChatGPT in 2022, there has been a surge in LLMs for diverse biomedical applications. This review examines the landscape of text-based biomedical LLM development, analyzing model characteristics (e.g., architecture), development processes (e.g., training strategy), and applications (e.g., chatbots). Following PRISMA guidelines, 82 articles were selected out of 5,512 articles since 2022 that met our rigorous criteria, including the requirement of using biomedical data when training LLMs. Findings highlight the predominant use of decoder-only architectures such as Llama 7B, prevalence of task-specific fine-tuning, and reliance on biomedical literature for training. Challenges persist in balancing data openness with privacy concerns and detailing model development, including computational resources used. Future efforts would benefit from multimodal integration, LLMs for specialized medical applications, and improved data sharing and model accessibility.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765290","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}
引用次数: 0
Clinical Text Generation: Are We There Yet?
IF 7
Annual Review of Biomedical Data Science Pub Date : 2025-03-18 DOI: 10.1146/annurev-biodatasci-103123-095202
Nicolas Hiebel, Olivier Ferret, Karën Fort, Aurélie Névéol
{"title":"Clinical Text Generation: Are We There Yet?","authors":"Nicolas Hiebel, Olivier Ferret, Karën Fort, Aurélie Névéol","doi":"10.1146/annurev-biodatasci-103123-095202","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-103123-095202","url":null,"abstract":"<p><p>Generative artificial intelligence (AI), operationalized as large language models, is increasingly used in the biomedical field to assist with a range of text processing tasks including text classification, information extraction, and decision support. In this article, we focus on the primary purpose of generative language models, namely the production of unstructured text. We review past and current methods used to generate text as well as methods for evaluating open text generation, i.e., in contexts where no reference text is available for comparison. We discuss clinical applications that can benefit from high quality, ethically designed text generation, such as clinical note generation and synthetic text generation in support of secondary use of health data. We also raise awareness of the risks involved with generative AI such as overconfidence in outputs due to anthropomorphism and the risk of representational and allocation harms due to biases.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658875","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}
引用次数: 0
Generative Artificial Intelligence in Medicine.
IF 7
Annual Review of Biomedical Data Science Pub Date : 2025-03-18 DOI: 10.1146/annurev-biodatasci-103123-095332
Divya Shanmugam, Monica Agrawal, Rajiv Movva, Irene Y Chen, Marzyeh Ghassemi, Maia Jacobs, Emma Pierson
{"title":"Generative Artificial Intelligence in Medicine.","authors":"Divya Shanmugam, Monica Agrawal, Rajiv Movva, Irene Y Chen, Marzyeh Ghassemi, Maia Jacobs, Emma Pierson","doi":"10.1146/annurev-biodatasci-103123-095332","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-103123-095332","url":null,"abstract":"<p><p>The increased capabilities of generative artificial intelligence (AI) have dramatically expanded its possible use cases in medicine. We provide a comprehensive overview of generative AI use cases for clinicians, patients, clinical trial organizers, researchers, and trainees. We then discuss the many challenges-including maintaining privacy and security, improving transparency and interpretability, upholding equity, and rigorously evaluating models-that must be overcome to realize this potential, as well as the open research directions they give rise to.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658878","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}
引用次数: 0
Genetic Studies Through the Lens of Gene Networks.
IF 7
Annual Review of Biomedical Data Science Pub Date : 2025-02-20 DOI: 10.1146/annurev-biodatasci-103123-095355
Marc Subirana-Granés, Jill Hoffman, Haoyu Zhang, Christina Akirtava, Sutanu Nandi, Kevin Fotso, Milton Pividori
{"title":"Genetic Studies Through the Lens of Gene Networks.","authors":"Marc Subirana-Granés, Jill Hoffman, Haoyu Zhang, Christina Akirtava, Sutanu Nandi, Kevin Fotso, Milton Pividori","doi":"10.1146/annurev-biodatasci-103123-095355","DOIUrl":"10.1146/annurev-biodatasci-103123-095355","url":null,"abstract":"<p><p>Understanding the genetic basis of complex traits is a longstanding challenge in the field of genomics. Genome-wide association studies have identified thousands of variant-trait associations, but most of these variants are located in noncoding regions, making the link to biological function elusive. While traditional approaches, such as transcriptome-wide association studies (TWAS), have advanced our understanding by linking genetic variants to gene expression, they often overlook gene-gene interactions. Here, we review current approaches to integrate different molecular data, leveraging machine learning methods to identify gene modules based on coexpression and functional relationships. These integrative approaches, such as PhenoPLIER, combine TWAS and drug-induced transcriptional profiles to effectively capture biologically meaningful gene networks. This integration provides a context-specific understanding of disease processes while highlighting both core and peripheral genes. These insights pave the way for novel therapeutic targets and enhance the interpretability of genetic studies in personalized medicine.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469408","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}
引用次数: 0
Evaluation and Regulation of Artificial Intelligence Medical Devices for Clinical Decision Support.
IF 7
Annual Review of Biomedical Data Science Pub Date : 2025-02-19 DOI: 10.1146/annurev-biodatasci-103123-095824
Gary E Weissman
{"title":"Evaluation and Regulation of Artificial Intelligence Medical Devices for Clinical Decision Support.","authors":"Gary E Weissman","doi":"10.1146/annurev-biodatasci-103123-095824","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-103123-095824","url":null,"abstract":"<p><p>Artificial intelligence (AI) methods were first developed nearly seven decades ago. Only in recent years have they demonstrated their potential to improve clinical care at the bedside. AI systems are now capable of interpreting, predicting, and even generating important medical information. AI medical devices share many similarities with traditional medical devices but also diverge from them in important ways. Despite widespread optimism and enthusiasm surrounding the use of such devices to improve care processes, patient outcomes, and the healthcare experience for patients, caregivers, and clinicians alike, little evidence exists so far for their effectiveness in practice. Even less is known about the safety or equity of AI medical devices. As with any new technology, this exciting time is accompanied by appropriate questions regarding if, how much, when, and who such AI systems really help. Different stakeholders, ranging from patients to clinicians to industry device developers, may have divergent preferences or assessments of risk and benefits, warranting an informed public discussion to guide emerging regulatory efforts. This review summarizes the rapidly evolving recent efforts and evidence related to the regulation and evaluation of AI medical devices and highlights opportunities for future work to ensure their effectiveness, safety, and equity.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143459781","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}
引用次数: 0
Foundation Models for Translational Cancer Biology.
IF 7
Annual Review of Biomedical Data Science Pub Date : 2025-01-29 DOI: 10.1146/annurev-biodatasci-103123-095633
Kevin K Tsang, Sophia Kivelson, Jose M Acitores Cortina, Aditi Kuchi, Jacob S Berkowitz, Hongyu Liu, Apoorva Srinivasan, Nadine A Friedrich, Yasaman Fatapour, Nicholas P Tatonetti
{"title":"Foundation Models for Translational Cancer Biology.","authors":"Kevin K Tsang, Sophia Kivelson, Jose M Acitores Cortina, Aditi Kuchi, Jacob S Berkowitz, Hongyu Liu, Apoorva Srinivasan, Nadine A Friedrich, Yasaman Fatapour, Nicholas P Tatonetti","doi":"10.1146/annurev-biodatasci-103123-095633","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-103123-095633","url":null,"abstract":"<p><p>Cancer remains a leading cause of death globally. The complexity and diversity of cancer-related datasets across different specialties pose challenges in refining precision medicine for oncology. Foundation models offer a promising solution. Trained on vast amounts of data, these models develop a broad understanding across a wide range of tasks. We examine the role of foundation models in domains relevant to cancer research, including natural language processing, computer vision, molecular biology, and cheminformatics. Through a review of state-of-the-art methods, we explore how these models have already advanced translational cancer research goals such as precision tumor classification and artificial intelligence-assisted surgery. We also discuss prospective advances in areas like early tumor detection, personalized cancer treatment, and drug discovery. This review provides researchers with a curated set of resources and methodologies, offers practitioners a deeper understanding of how these models enhance cancer care, and points to opportunities for future applications of foundation models in cancer research.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068152","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}
引用次数: 0
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