Annual Review of Biomedical Data Science最新文献_第5页

Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare. 超越多项选择的准确性：在医疗保健中实现大型语言模型的现实挑战。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub 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

引用次数: 0

The Expanding Landscape of Neural Architectures and Their Impact in Biomedicine. 神经结构的扩展景观及其在生物医学中的影响。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 DOI: 10.1146/annurev-biodatasci-103023-050856

Zijun Frank Zhang, Huixin Zhan, Tinghui Wu, Robert Burns, Jasreet Hundal, Helio A Costa

{"title":"The Expanding Landscape of Neural Architectures and Their Impact in Biomedicine.","authors":"Zijun Frank Zhang, Huixin Zhan, Tinghui Wu, Robert Burns, Jasreet Hundal, Helio A Costa","doi":"10.1146/annurev-biodatasci-103023-050856","DOIUrl":"10.1146/annurev-biodatasci-103023-050856","url":null,"abstract":"Deep learning and artificial intelligence (AI) have seen explosive growth and success in biomedical applications in the last decade, largely due to the rapid development of deep neural networks and their underlying neural network (NN) architectures. Here, we explore biomedical deep learning and AI from the specific perspective of NN architectures. We discuss widely varying design principles of NN architectures, their use in particular biomedical applications, and the assumptions (often hidden) built into them. We explore neural architecture search techniques that automate the design of NN topology to optimize task performance. Advanced neural architectures are being developed for both molecular and healthcare applications, employing elements of graph networks, transformers, and interpretable NNs, and we discuss and summarize the design considerations and unique advantages of each architecture. Future advances will include the employment of multimodal language models and smaller highly focused mechanistic models that build on the success of today's large models.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"8 1","pages":"101-124"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144822754","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

Biomedical Natural Language Processing in the Era of Large Language Models. 大语言模型时代的生物医学自然语言处理。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-04-17 DOI: 10.1146/annurev-biodatasci-103123-095406

Naoto Usuyama, Cliff Wong, Sheng Zhang, Tristan Naumann, Hoifung Poon

{"title":"Biomedical Natural Language Processing in the Era of Large Language Models.","authors":"Naoto Usuyama, Cliff Wong, Sheng Zhang, Tristan Naumann, Hoifung Poon","doi":"10.1146/annurev-biodatasci-103123-095406","DOIUrl":"10.1146/annurev-biodatasci-103123-095406","url":null,"abstract":"Biomedicine has rapidly digitized over recent decades, from genomic sequencing to electronic medical records. Now, the rise of large language models (LLMs) is driving a generative artificial intelligence (AI) revolution in natural language processing (NLP). Together, these trends create unprecedented possibilities to optimize patient care and accelerate biomedical discovery. Biomedical NLP already boosts productivity by automating labor-intensive tasks such as knowledge extraction and medical abstraction. Emerging approaches promise creativity gain, surpassing standard healthcare practices and uncovering emergent capabilities through Web-scale biomedical knowledge and population-level patient data. However, LLMs remain prone to hallucinations and omissions, and ensuring compliance and safety is vital in order to do no harm. Incorporating diverse modalities such as imaging and genomics is also essential for comprehensive solutions. We review these challenges and opportunities in biomedical NLP, offering historical context, surveying the current state of the art, and exploring frontiers for AI researchers and biomedical practitioners.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"471-490"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052846","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

Curriculum Design in an Evolving Field: Perspectives on Biomedical Data Science from Stanford. 不断发展的领域中的课程设计：斯坦福大学生物医学数据科学的视角。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-04-09 DOI: 10.1146/annurev-biodatasci-090624-022951

Christine Y Yeh, Dennis P Wall, Karen Matthys, Chiara Sabatti, Julia A Palacios

{"title":"Curriculum Design in an Evolving Field: Perspectives on Biomedical Data Science from Stanford.","authors":"Christine Y Yeh, Dennis P Wall, Karen Matthys, Chiara Sabatti, Julia A Palacios","doi":"10.1146/annurev-biodatasci-090624-022951","DOIUrl":"10.1146/annurev-biodatasci-090624-022951","url":null,"abstract":"In recent decades, there has been an explosion of data streams spanning the entire spectrum of biomedicine, opening novel opportunities to tackle biological and medical research questions, increasing our ability to provide effective and efficient health care. In parallel, augmented computational power has allowed the development and deployment of quantitative approaches at unprecedented scales. To effectively take advantage of this progress, it is important to invest in the training of a new generation of biomedical data scientists. Designing a graduate curriculum in the backdrop of a rapidly changing landscape of data, methods, and computing power demands flexibility and openness to adaptation. At the same time, we strive to ensure that the students acquire foundational competencies that might fuel productive and evolving careers, without being constrained to and defined by a niche trendy topic. We offer here a view of graduate training in biomedical data science from the standpoint of our experience at Stanford University. We conclude with a series of open challenges, the answers to which we believe will shape training in biomedical data science.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"341-354"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144027038","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. 临床决策支持人工智能医疗器械的评价与规范

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub 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":"10.1146/annurev-biodatasci-103123-095824","url":null,"abstract":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"81-99"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143459781","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}

引用次数: 0

Spatial Transcriptomics Brings New Challenges and Opportunities for Trajectory Inference. 空间转录组学为轨迹推断带来新的挑战和机遇

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2024-11-14 DOI: 10.1146/annurev-biodatasci-040324-030052

Matthieu Heitz, Yujia Ma, Sharvaj Kubal, Geoffrey Schiebinger

引用次数: 0

Generative Artificial Intelligence in Medicine. 医学中的生成式人工智能。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub 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

引用次数: 0

Algorithm-Based Clinical Decision Support: Evolving Regulatory Landscape and Best Practices for Local Oversight. 基于算法的临床决策支持：不断发展的监管环境和地方监督的最佳实践。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-04-21 DOI: 10.1146/annurev-biodatasci-103123-094601

Anthony L Lin, Amanda B Parrish, Michael Cary, Christina Silcox, Suresh Balu, J Eric Jelovsek, Cara O'Brien, Michael Pencina, Eric Poon, Nicoleta J Economou-Zavlanos

{"title":"Algorithm-Based Clinical Decision Support: Evolving Regulatory Landscape and Best Practices for Local Oversight.","authors":"Anthony L Lin, Amanda B Parrish, Michael Cary, Christina Silcox, Suresh Balu, J Eric Jelovsek, Cara O'Brien, Michael Pencina, Eric Poon, Nicoleta J Economou-Zavlanos","doi":"10.1146/annurev-biodatasci-103123-094601","DOIUrl":"10.1146/annurev-biodatasci-103123-094601","url":null,"abstract":"The potential of algorithm-based clinical decision support (CDS) in healthcare continues to increase with the growing field of artificial intelligence (AI)-enabled CDS. The use of these technologies to support clinicians, patients, and health systems is still quite new, and to date, implementors and regulators are still identifying the best processes and practices to ensure the effective, safe, and equitable use of these technology solutions. To assist individuals and organizations interested in implementation of algorithm-based CDS and AI-enabled CDS in healthcare, this article reviews the important regulatory decisions that form the landscape within which algorithm-based CDS has emerged, modern governance frameworks used to oversee these CDS systems, nuances in evaluation and monitoring throughout the CDS life cycle, best practices for real-world implementation, safety and equity considerations, and avenues for future collaboration and innovation.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"491-507"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022132","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. 重新审视技术偏见缓解战略。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-04-08 DOI: 10.1146/annurev-biodatasci-103123-095737

Abdoul Jalil Djiberou Mahamadou, Artem A Trotsyuk

引用次数: 0

Genetic Studies Through the Lens of Gene Networks. 基因网络视角下的基因研究。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub 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":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"125-147"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469408","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}

引用次数: 0