Annual Review of Biomedical Data Science最新文献_第4页

Conditional Generative Models for Synthetic Tabular Data: Applications for Precision Medicine and Diverse Representations. 合成表格数据的条件生成模型：精准医疗和多样化表示的应用。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-01-14 DOI: 10.1146/annurev-biodatasci-103123-094844

Kara Liu, Russ B Altman

{"title":"Conditional Generative Models for Synthetic Tabular Data: Applications for Precision Medicine and Diverse Representations.","authors":"Kara Liu, Russ B Altman","doi":"10.1146/annurev-biodatasci-103123-094844","DOIUrl":"10.1146/annurev-biodatasci-103123-094844","url":null,"abstract":"Tabular medical datasets, like electronic health records (EHRs), biobanks, and structured clinical trial data, are rich sources of information with the potential to advance precision medicine and optimize patient care. However, real-world medical datasets have limited patient diversity and cannot simulate hypothetical outcomes, both of which are necessary for equitable and effective medical research. Fueled by recent advancements in machine learning, generative models offer a promising solution to these data limitations by generating enhanced synthetic data. This review highlights the potential of conditional generative models (CGMs) to create patient-specific synthetic data for a variety of precision medicine applications. We survey CGM approaches that tackle two medical applications: correcting for data representation biases and simulating digital health twins. We additionally explore how the surveyed methods handle modeling tabular medical data and briefly discuss evaluation criteria. Finally, we summarize the technical, medical, and ethical challenges that must be addressed before CGMs can be effectively and safely deployed in the medical field.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"21-49"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142984817","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. 药物安全研究中的综合数据科学：经验、挑战和展望。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-04-01 DOI: 10.1146/annurev-biodatasci-103123-095506

Ferran Sanz

引用次数: 0

Integrating Artificial Intelligence in Dermatological Cancer Screening and Diagnosis: Efficacy, Challenges, and Future Directions. 将人工智能应用于皮肤病癌症筛查和诊断：疗效、挑战和未来方向。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-05-01 DOI: 10.1146/annurev-biodatasci-103123-094521

Vivian Utti, Vasiliki Bikia, Ank A Agarwal, Roxana Daneshjou

{"title":"Integrating Artificial Intelligence in Dermatological Cancer Screening and Diagnosis: Efficacy, Challenges, and Future Directions.","authors":"Vivian Utti, Vasiliki Bikia, Ank A Agarwal, Roxana Daneshjou","doi":"10.1146/annurev-biodatasci-103123-094521","DOIUrl":"10.1146/annurev-biodatasci-103123-094521","url":null,"abstract":"Skin cancer is the most common cancer in the United States, with incidence rates continuing to rise both nationally and globally, posing significant health and economic burdens. These challenges are compounded by shortages in dermatological care and barriers to insurance access. To address these gaps, artificial intelligence (AI) and deep learning technologies offer promising solutions, enhancing skin cancer screening and diagnosis. AI has the potential to improve diagnostic accuracy and expand access to care, but significant challenges restrict its deployment. These challenges include clinical validation, algorithmic bias, regulatory oversight, and patient acceptance. Ethical concerns, such as disparities in access and fairness of AI algorithms, also require attention. In this review, we explore these limitations and outline future directions, including advancements in teledermatology and vision-language models (VLMs). Future research should focus on improving VLM reliability and interpretability and developing systems capable of integrating clinical context with dermatological images in a way that assists, rather than replaces, clinicians in making more accurate, timely diagnoses.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"591-603"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001083","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

Network-Based Approaches for Drug Target Identification. 基于网络的药物靶标识别方法。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-04-16 DOI: 10.1146/annurev-biodatasci-101424-120950

Thodoris Koutsandreas, Kalliopi Tsafou, Heiko Horn, Ian Barrett, Evangelia Petsalaki

引用次数: 0

Artificial Intelligence in Pathology: Advancing Large Models for Scalable Applications. 病理学中的人工智能：推进可扩展应用的大型模型。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 DOI: 10.1146/annurev-biodatasci-103123-095814

Zhiping Xiao, Bin Feng, Junwei Yang, Gongbo Sun, Yuxi Shen, Shengyuan Xu, Lina Yang, Hanwen Xu, Ming Zhang, Sheng Wang

{"title":"Artificial Intelligence in Pathology: Advancing Large Models for Scalable Applications.","authors":"Zhiping Xiao, Bin Feng, Junwei Yang, Gongbo Sun, Yuxi Shen, Shengyuan Xu, Lina Yang, Hanwen Xu, Ming Zhang, Sheng Wang","doi":"10.1146/annurev-biodatasci-103123-095814","DOIUrl":"10.1146/annurev-biodatasci-103123-095814","url":null,"abstract":"The rapid development of artificial intelligence (AI) has had a significant impact on medical research, introducing new possibilities for pathology studies. There is a recent trend of applying large-scale AI models to many fields, and this trend has given rise to the pathology foundation models and pathology ensemble models. Large models in pathology are not standalone innovations; they build upon a legacy where AI has consistently played a vital role in pathology studies long before their advent. Numerous pathology datasets and AI models have been developed to support advancements in the field, with these combined efforts paving the way for the emergence of large models in pathology. AI greatly enhances pathology studies, yet its widespread use in sensitive applications also raises significant ethical concerns, including privacy risks. In this review, we summarize the datasets and models that are useful to pathology studies, with a particular focus on how they illuminate the path toward large-scale applications.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"8 1","pages":"149-171"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144822750","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

From Prediction to Prescription: Machine Learning and Causal Inference for the Heterogeneous Treatment Effect. 从预测到处方：异质治疗效果的机器学习和因果推理。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-04-09 DOI: 10.1146/annurev-biodatasci-103123-095750

Judith Abécassis, Élise Dumas, Julie Alberge, Gaël Varoquaux

引用次数: 0

Generative Artificial Intelligence: Implications for Biomedical and Health Professions Education. 生成式人工智能：对生物医学和卫生专业教育的影响。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-04-09 DOI: 10.1146/annurev-biodatasci-103123-094756

William Hersh

引用次数: 0

Leveraging Unstructured Data in Electronic Health Records to Detect Adverse Events from Pediatric Drug Use: A Scoping Review. 利用电子健康记录中的非结构化数据来检测儿童药物使用的不良事件：范围审查。

IF 8.1

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 DOI: 10.1146/annurev-biodatasci-111224-124530

Su Golder, Karen O'Connor, Guillermo Lopez-Garcia, Nicholas P Tatonetti, Graciela Gonzalez-Hernandez

{"title":"Leveraging Unstructured Data in Electronic Health Records to Detect Adverse Events from Pediatric Drug Use: A Scoping Review.","authors":"Su Golder, Karen O'Connor, Guillermo Lopez-Garcia, Nicholas P Tatonetti, Graciela Gonzalez-Hernandez","doi":"10.1146/annurev-biodatasci-111224-124530","DOIUrl":"10.1146/annurev-biodatasci-111224-124530","url":null,"abstract":"Adverse drug events (ADEs) in pediatric populations pose significant public health challenges, yet research on their detection and monitoring remains limited. This scoping review evaluates the use of unstructured data from electronic health records (EHRs) to identify ADEs in children. We searched six databases, including MEDLINE, Embase, and IEEE Xplore, in September 2024. From 984 records, only nine studies met our inclusion criteria, indicating a significant gap in research toward identifying ADEs in children. We found that unstructured data in EHRs can indeed be of value and enhance pediatric pharmacovigilance, although their use has been so far very limited. Traditional natural language processing methods have been employed to extract ADEs, but the approaches utilized face challenges in generalizability and context interpretation. These challenges could be addressed with recent advances in transformer-based models and large language models, unlocking the use of EHR data at scale for pediatric pharmacovigilance.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"8 1","pages":"227-250"},"PeriodicalIF":8.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144822751","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

Methylation Data Analysis and Interpretation. 甲基化数据分析与解释。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 DOI: 10.1146/annurev-biodatasci-120924-091033

Yuehua Zhu, Weiguang Mao, Rezwan Hosseini, Maria Chikina

引用次数: 0

The Development Landscape of Large Language Models for Biomedical Applications. 生物医学应用的大型语言模型的发展前景。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub 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":"10.1146/annurev-biodatasci-102224-074736","url":null,"abstract":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"251-274"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12372014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765290","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