Annual Review of Biomedical Data Science最新文献

Mapping, Modeling, and Reprogramming Cell-Fate Decision-Making Systems. 绘图、建模和重编程细胞命运决策系统。

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Annual Review of Biomedical Data Science Pub Date : 2025-05-01 DOI: 10.1146/annurev-biodatasci-101424-121439

Lucy Ham, Taylor E Woodward, Megan A Coomer, Michael P H Stumpf

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。

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Annual Review of Biomedical Data Science Pub 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":"https://doi.org/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":""},"PeriodicalIF":7.0,"publicationDate":"2025-05-01","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

Quantum Computing for Photosensitizer Design in Photodynamic Therapy. 光动力疗法中光敏剂设计的量子计算。

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Annual Review of Biomedical Data Science Pub Date : 2025-05-01 DOI: 10.1146/annurev-biodatasci-103123-095644

Hope Zehr, Alberto Baiardi, Francesco Tacchino, Anthony Gandon, Laurin E Fischer, Yue Xu, Frank P DiFilippo, Leonardo Guidoni, Pi A B Haase, Walter N Talarico, Martina Stella, Fabio Tarocco, Anton Nykänen, Aaron Fitzpatrick, Aaron Miller, Leander Thiessen, Stefan Knecht, Elsi-Mari Borrelli, Sabrina Maniscalco, Fabijan Pavošević, Ivano Tavernelli, Edward Maytin, Vijay Krishna

{"title":"Quantum Computing for Photosensitizer Design in Photodynamic Therapy.","authors":"Hope Zehr, Alberto Baiardi, Francesco Tacchino, Anthony Gandon, Laurin E Fischer, Yue Xu, Frank P DiFilippo, Leonardo Guidoni, Pi A B Haase, Walter N Talarico, Martina Stella, Fabio Tarocco, Anton Nykänen, Aaron Fitzpatrick, Aaron Miller, Leander Thiessen, Stefan Knecht, Elsi-Mari Borrelli, Sabrina Maniscalco, Fabijan Pavošević, Ivano Tavernelli, Edward Maytin, Vijay Krishna","doi":"10.1146/annurev-biodatasci-103123-095644","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-103123-095644","url":null,"abstract":"Use of light in healthcare is evolving with increasing applications of photodynamic therapy (PDT) for treating various cancers. PDT utilizes light-activated molecules called photosensitizers (PSs) that generate reactive oxygen species (ROSs) to induce tumor cell apoptosis and necrosis. However, the use of PDT is limited by the availability of PSs that can be activated by deep tissue-penetrating near-infrared light, exhibit low dark toxicity, and produce ROSs efficiently. Here we review the different categories of PS currently used in clinical or preclinical trials and highlight the significance of advanced computational methods, including density functional and wave function-based quantum chemistry, for understanding the molecular mechanisms involved in PS activation. Despite advancements in classical computational techniques, the complexities of excited state dynamics in highly correlated molecular systems demand innovative simulation approaches such as quantum computing. We propose that quantum computing holds promise for accurately modeling the excited-state properties of PSs to optimize their design and broaden clinical applications.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144017049","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

Embedding Methods for Electronic Health Record Research. 电子健康档案研究的嵌入方法。

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Annual Review of Biomedical Data Science Pub Date : 2025-05-01 DOI: 10.1146/annurev-biodatasci-103123-094729

Justin Kauffman, Riccardo Miotto, Eyal Klang, Anthony Costa, Beau Norgeot, Marinka Zitnik, Shameer Khader, Fei Wang, Girish N Nadkarni, Benjamin S Glicksberg

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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。

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Annual Review of Biomedical Data Science Pub 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":"https://doi.org/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":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-21","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}

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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。

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Annual Review of Biomedical Data Science Pub 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":"https://doi.org/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":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-17","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}

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The TITAN-X Platform Integrates Big Data, Artificial Intelligence, Bioinformatics, and Advanced Computational Modeling to Understand Immune Responses and Develop the Next Wave of Precision Medicines. TITAN-X平台集成了大数据、人工智能、生物信息学和先进的计算建模，以了解免疫反应并开发下一波精准医疗。

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Annual Review of Biomedical Data Science Pub Date : 2025-04-16 DOI: 10.1146/annurev-biodatasci-103123-094804

Ryan Baker, Josep Bassaganya-Riera, Nuria Tubau-Juni, Andrew J Leber, Raquel Hontecillas

{"title":"The TITAN-X Platform Integrates Big Data, Artificial Intelligence, Bioinformatics, and Advanced Computational Modeling to Understand Immune Responses and Develop the Next Wave of Precision Medicines.","authors":"Ryan Baker, Josep Bassaganya-Riera, Nuria Tubau-Juni, Andrew J Leber, Raquel Hontecillas","doi":"10.1146/annurev-biodatasci-103123-094804","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-103123-094804","url":null,"abstract":"The TITAN-X Precision Medicine Platform was engineered to rapidly, fully, and efficiently utilize large-scale immunology datasets, including public data, in drug discovery and development. TITAN-X integrates big data with artificial intelligence (AI), bioinformatics, and advanced computational modeling to seamlessly transition from early target discovery to clinical testing of new therapeutics, developing biomarker-driven precision medicines tailored to specific patient populations. We illustrate the capabilities of TITAN-X through four case studies, demonstrating its use in computationally driven target discovery; characterization of novel immunometabolic mechanisms in infectious, inflammatory, and autoimmune diseases; and identification of biomarker signatures for patient stratification in clinical trials designed to maximize therapeutic efficacy and safety. Data-driven and AI-powered approaches like TITAN-X are enhancing the pace of drug development, reducing costs, tailoring treatments, and increasing the probability of success in clinical trials.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144039901","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}

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

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Annual Review of Biomedical Data Science Pub 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

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。

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Annual Review of Biomedical Data Science Pub 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。

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

William Hersh

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