{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T23:38:43Z","timestamp":1789601923140,"version":"build-2803163510"},"reference-count":55,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:00:00Z","timestamp":1783036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) is becoming central to genomics and multi-omics, but its concepts, architectures, applications, evaluation standards, and translational requirements remain fragmented. This scoping review mapped how AI is defined and operationalized in genomic science, including machine learning, deep learning, graph-based methods, foundation models, and large language models, and synthesized their data modalities, applications, evaluation practices, interpretability strategies, and governance challenges.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a PRISMA-ScR scoping review with Joanna Briggs Institute guidance. Eligible studies applied AI to genomics or closely allied omics in research, clinical, or public health contexts. MEDLINE\/PubMed, Embase, and supplementary registers were searched from January 2001 to 3 September 2025 without language restrictions. Records were screened in duplicate, and standardized items were extracted, including AI concept or method family, omics modality, task, metrics, interpretability, governance, and deployment considerations. Methodological reporting and quality were appraised using design-appropriate JBI tools and summarized descriptively as a normalized 0%\u2013100% checklist-fulfillment index.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>From 3,785 records, 1,040 studies were included. Publication remained sparse until 2017 and then expanded steeply, with more than 90% appearing from 2018 onward. The normalized JBI checklist-fulfillment index was modest overall (mean 35.3%, SD 20.1; range 7.5%\u201387.5%) and was interpreted descriptively, not as a directly comparable quality score across designs. Conceptually, the field has moved from feature-engineered statistical learning toward representation learning systems modeling nucleotide sequences, regulatory context, single-cell states, multi-omics profiles, biomedical text, and clinical-genomic knowledge. Applications concentrated on variant interpretation, regulatory genomics, multi-omics integration, single-cell analysis, pathology\/radiology-genomics fusion, and genomic decision support, with increasing use of deep learning, graph models, foundation models, and LLMs. Calibration, external validation, mechanistic interpretability, ancestry-aware fairness, privacy protection, and deployment models for sensitive genomic data were unevenly reported; prospective multisite evaluations were rare.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>AI in genomics has scaled rapidly since 2017\u20132018, but translation remains constrained by heterogeneous concepts, inconsistent benchmarks, incomplete reporting, and limited governance. Priorities include biologically meaningful benchmarks; calibrated uncertainty for genomic decision support; mechanism-linked interpretability; ancestry- and site-aware validation; privacy-preserving analysis of sensitive genomic data; and human oversight for variant interpretation, precision medicine, and public health genomics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic Review Registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>https:\/\/osf.io\/uexzh<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fbinf.2026.1829576","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T05:39:23Z","timestamp":1783057163000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial intelligence for genomic science: a scoping review of concepts, architectures, applications, and open challenges"],"prefix":"10.3389","volume":"6","author":[{"given":"Wellington Francisco","family":"Rodrigues","sequence":"first","affiliation":[{"name":"Molecular and Computational Biology of Fungi Laboratory, Department of Microbiology, Instituto de Ci\u00eancias Biol\u00f3gicas, Universidade Federal de Minas Gerais","place":["Belo Horizonte, Minas Gerais, Brazil"]},{"name":"Laboratory of Genetics, Biotechnology Institute, Federal University of Uberl\u00e2ndia","place":["Uberl\u00e2ndia, Minas Gerais, Brazil"]},{"name":"Multidisciplinary Laboratory of Scientific Evidence, University Center of Mineiros (Unifimes)","place":["Mineiros, Goi\u00e1s, Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariana T. D.","family":"Parise","sequence":"additional","affiliation":[{"name":"Molecular and Computational Biology of Fungi Laboratory, Department of Microbiology, Instituto de Ci\u00eancias Biol\u00f3gicas, Universidade Federal de Minas Gerais","place":["Belo Horizonte, Minas Gerais, Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doglas","family":"Parise","sequence":"additional","affiliation":[{"name":"Molecular and Computational Biology of Fungi Laboratory, Department of Microbiology, Instituto de Ci\u00eancias Biol\u00f3gicas, Universidade Federal de Minas Gerais","place":["Belo Horizonte, Minas Gerais, Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucas Moraes","family":"dos Santos","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Universidade Federal de Minas Gerais","place":["Belo Horizonte, Minas Gerais, Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priscyla dos Santos","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Expertise Centre for Leptospirosis \/ WOAH Reference Laboratory for Leptospirosis, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers","place":["Amsterdam, Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paula","family":"Ristow","sequence":"additional","affiliation":[{"name":"Expertise Centre for Leptospirosis \/ WOAH Reference Laboratory for Leptospirosis, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers","place":["Amsterdam, Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariana Santos","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Molecular and Computational Biology of Fungi Laboratory, Department of Microbiology, Instituto de Ci\u00eancias Biol\u00f3gicas, Universidade Federal de Minas Gerais","place":["Belo Horizonte, Minas Gerais, Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasco Ariston de Carvalho","family":"Azevedo","sequence":"additional","affiliation":[{"name":"Laboratory of Cellular and Molecular Genetics, Institute of Biological Sciences, Universidade Federal de Minas Gerais","place":["Belo Horizonte, Minas Gerais, Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siomar de Castro","family":"Soares","sequence":"additional","affiliation":[{"name":"Department of Microbiology, Immunology and Parasitology, Institute of Biological and Natural Sciences, Federal University of Tri\u00e2ngulo Mineiro","place":["Uberaba, Minas Gerais, Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raquel Cardoso de Melo","family":"Minardi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Universidade Federal de Minas Gerais","place":["Belo Horizonte, Minas Gerais, Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arist\u00f3teles","family":"Go\u00e9s-Neto","sequence":"additional","affiliation":[{"name":"Molecular and Computational Biology of Fungi Laboratory, Department of Microbiology, Instituto de Ci\u00eancias Biol\u00f3gicas, Universidade Federal de Minas Gerais","place":["Belo Horizonte, Minas Gerais, Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1186\/s12859-023-05596-3","article-title":"Performance analysis of conventional and AI-based variant callers using short and long reads","volume":"24","author":"Abdelwahab","year":"2023","journal-title":"BMC Bioinforma."},{"key":"B2","doi-asserted-by":"publisher","first-page":"878","DOI":"10.15252\/msb.20156651","article-title":"Deep learning for computational biology","volume":"12","author":"Angermueller","year":"2016","journal-title":"Mol. Syst. Biol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1038\/s41592-021-01252-x","article-title":"Effective gene expression prediction from sequence by integrating long-range interactions","volume":"18","author":"Avsec","year":"2021","journal-title":"Nat. Methods"},{"key":"B4","doi-asserted-by":"publisher","first-page":"2380","DOI":"10.1038\/s41467-026-69102-y","article-title":"scLong: a billion-parameter foundation model for capturing long-range gene context in single-cell transcriptomics","volume":"17","author":"Bai","year":"2026","journal-title":"Nat. Commun."},{"key":"B5","doi-asserted-by":"publisher","first-page":"bbaf355","DOI":"10.1093\/bib\/bbaf355","article-title":"A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches","volume":"26","author":"Bai\u00e3o","year":"2025","journal-title":"Briefings Bioinforma."},{"key":"B6","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s13040-024-00391-z","article-title":"Deep learning-based approaches for multi-omics data integration and analysis","volume":"17","author":"Ballard","year":"2024","journal-title":"BioData Min."},{"key":"B7","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1146\/annurev-biodatasci-022020-021940","article-title":"Identifying regulatory elements via deep learning","volume":"3","author":"Barshai","year":"2020","journal-title":"Annu. Rev. Biomed. Data Sci."},{"key":"B8","doi-asserted-by":"publisher","first-page":"100943","DOI":"10.1016\/j.landig.2025.100943","article-title":"CARDBiomedBench: a benchmark for evaluating the performance of large language models in biomedical research","volume":"8","author":"Bianchi","year":"2026","journal-title":"Lancet Digital Health"},{"key":"B9","doi-asserted-by":"publisher","first-page":"15","DOI":"10.3390\/genes15121650","article-title":"Federated learning: breaking Down barriers in global genomic research","volume":"22","author":"Calvino","year":"2024","journal-title":"Genes (Basel). Dec"},{"key":"B10","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1080\/15265161.2023.2276161","article-title":"Consideration and disclosure of group risks in genomics and other data-centric research: does the common rule need revision?","volume":"25","author":"Chapman","year":"2025","journal-title":"Am. J. Bioeth."},{"key":"B11","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.ygeno.2012.04.003","article-title":"Random forests for genomic data analysis","volume":"99","author":"Chen","year":"2012","journal-title":"Genomics. 2012\/06\/01\/"},{"key":"B12","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1016\/j.ccell.2022.07.004","article-title":"Pan-cancer integrative histology-genomic analysis via multimodal deep learning","volume":"40","author":"Chen","year":"2022","journal-title":"Cancer Cell"},{"key":"B13","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s12687-023-00678-4","article-title":"Ethics of artificial intelligence in prenatal and pediatric genomic medicine","volume":"15","author":"Coghlan","year":"2024","journal-title":"J. Community Genet. Feb"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.1038\/s41592-024-02201-0","article-title":"scGPT: toward building a foundation model for single-cell multi-omics using generative AI","volume":"21","author":"Cui","year":"2024","journal-title":"Nat. Methods"},{"key":"B15","doi-asserted-by":"publisher","first-page":"523679","DOI":"10.1101\/2023.01.11.523679","article-title":"The nucleotide transformer: building and evaluating robust foundation models for","volume":"2023","author":"Dalla-Torre","year":"2024","journal-title":"Hum. Genomics. Biorxiv."},{"key":"B16","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1186\/s13073-019-0689-8","article-title":"Artificial intelligence in clinical and genomic diagnostics","volume":"11","author":"Dias","year":"2019","journal-title":"Genome Med."},{"key":"B17","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1016\/j.gim.2021.10.019","article-title":"Phenotype based prediction of exome sequencing outcome using machine learning for neurodevelopmental disorders","volume":"24","author":"Dingemans","year":"2022","journal-title":"Genet. Med."},{"key":"B18","doi-asserted-by":"publisher","first-page":"106424","DOI":"10.1016\/j.ijmedinf.2026.106424","article-title":"Precision grounding: augmenting large language models with evidence-based databases for trustworthy genetic variant summarization","volume":"214","author":"Du","year":"2026","journal-title":"Int. J. Med. Inform."},{"key":"B19","doi-asserted-by":"publisher","first-page":"gkae1310","DOI":"10.1093\/nar\/gkae1310","article-title":"GENA-LM: a family of open-source foundational DNA language models for long sequences","volume":"53","author":"Fishman","year":"2025","journal-title":"Nucleic Acids Res."},{"key":"B20","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/s44257-024-00017-y","article-title":"Artificial intelligence applications to genomic data in cancer research: a review of recent trends and emerging areas","volume":"2","author":"Frasca","year":"2024","journal-title":"Discov. Anal."},{"key":"B21","doi-asserted-by":"publisher","first-page":"1098439","DOI":"10.3389\/fgene.2023.1098439","article-title":"Ethical layering in AI-driven polygenic risk scores-new complexities, new challenges","volume":"14","author":"Fritzsche","year":"2023","journal-title":"Front. Genet."},{"key":"B22","first-page":"55","article-title":"The artificial intelligence and machine learning in lung cancer immunotherapy","volume-title":"J. Hematol. Oncol.","author":"Gao","year":""},{"key":"B23","doi-asserted-by":"publisher","first-page":"32","DOI":"10.2202\/1544-6115.1691","article-title":"Random forests for genetic association studies","volume":"10","author":"Goldstein","year":"2011","journal-title":"Stat. Appl. Genet. Mol. Biol."},{"key":"B24","doi-asserted-by":"publisher","first-page":"e1230","DOI":"10.1002\/cl2.1230","article-title":"PRISMA2020: an R package and shiny app for producing PRISMA 2020\u2010compliant flow diagrams, with interactivity for optimised digital transparency and open synthesis","volume":"18","author":"Haddaway","year":"2022","journal-title":"Campbell Systematic Reviews"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1016\/j.tibtech.2019.05.007","article-title":"Accelerating climate resilient plant breeding by applying next-generation artificial intelligence","volume":"37","author":"Harfouche","year":"2019","journal-title":"Trends Biotechnol. Nov."},{"key":"B26","doi-asserted-by":"publisher","first-page":"41","DOI":"10.21873\/cgp.20063","article-title":"Applications of support vector machine (SVM) learning in cancer genomics","volume":"15","author":"Huang","year":"2018","journal-title":"Cancer Genomics - proteomics."},{"key":"B27","doi-asserted-by":"publisher","first-page":"btae075","DOI":"10.1093\/bioinformatics\/btae075","article-title":"GeneGPT: augmenting large language models with domain tools for improved access to biomedical information","volume":"40","author":"Jin","year":"2024","journal-title":"Bioinformatics"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2412.04075","article-title":"Does your model understand genes? A benchmark of gene properties for biological and text models","author":"Kan-Tor","year":"2024","journal-title":"ArXiv.abs\/2412.04075"},{"key":"B29","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1093\/jamia\/ocaa023","article-title":"EXpectation propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare\/genomics predictive model learning","volume":"27","author":"Kuo","year":"2020","journal-title":"J. Am. Med. Inf. Assoc."},{"key":"B30","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.3389\/fonc.2020.01030","article-title":"Integrated multi-omics analyses in oncology: a review of machine learning methods and tools","volume":"10","author":"Nicora","year":"2020","journal-title":"Front. Oncol."},{"key":"B31","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1186\/s13059-023-02985-y","article-title":"ExplaiNN: interpretable and transparent neural networks for genomics","volume":"24","author":"Novakovsky","year":"2023","journal-title":"Genome Biol."},{"key":"B32","doi-asserted-by":"publisher","first-page":"e1004754","DOI":"10.1371\/journal.pgen.1004754","article-title":"Regularized machine learning in the genetic prediction of complex traits","volume":"10","author":"Okser","year":"2014","journal-title":"PLoS Genet. Nov."},{"key":"B33","volume-title":"Updating Guidance for Reporting Systematic Reviews: Development of the PRISMA 2020 Statement","author":"Page","year":"2020"},{"key":"B34","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1634\/theoncologist.2017-0170","article-title":"Enhancing next-generation sequencing-guided cancer care through cognitive computing","volume":"23","author":"Patel","year":"2018","journal-title":"Oncologist"},{"key":"B35","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1038\/nbt.4235","article-title":"A universal SNP and small-indel variant caller using deep neural networks","volume":"36","author":"Poplin","year":"2018","journal-title":"Nat. Biotechnol."},{"key":"B36","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.cllc.2023.12.012","article-title":"APOLLO 11 Project, consortium in advanced lung cancer patients treated with innovative therapies: integration of real-world data and translational research","volume":"25","author":"Prelaj","year":"2024","journal-title":"Clin. Lung Cancer"},{"key":"B37","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1089\/bio.2022.0042","article-title":"Leveraging algorithms to improve decision-making workflows for genomic data access and management","volume":"20","author":"Rahimzadeh","year":"2022","journal-title":"Biopreserv Biobank. Oct."},{"key":"B38","doi-asserted-by":"publisher","first-page":"107739","DOI":"10.1016\/j.biotechadv.2021.107739","article-title":"Using machine learning approaches for multi-omics data analysis: a review","volume":"49","author":"Reel","year":"2021","journal-title":"Biotechnol. Adv."},{"key":"B39","doi-asserted-by":"publisher","first-page":"648850","DOI":"10.1101\/2025.04.14.648850","article-title":"Scaling large language models for next-generation single-cell analysis","volume":"2014","author":"Rizvi","year":"2025","journal-title":"bioRxiv"},{"key":"B40","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3390\/v12090972","article-title":"Artificial intelligence-assisted loop mediated isothermal amplification (AI-LAMP) for rapid detection of SARS-CoV-2","volume":"1","author":"Rohaim","year":"2020","journal-title":"Viruses"},{"key":"B41","doi-asserted-by":"publisher","first-page":"104429","DOI":"10.1016\/j.jbi.2023.104429","article-title":"A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases","volume":"143","author":"Roman-Naranjo","year":"2023","journal-title":"J. Biomed. Inf. Jul"},{"key":"B42","doi-asserted-by":"publisher","first-page":"103663","DOI":"10.1101\/103663","article-title":"Reverse-complement parameter sharing improves deep learning models for genomics","author":"Shrikumar","year":"2017","journal-title":"bioRxiv"},{"key":"B43","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1146\/annurev-genom-021623-024727","article-title":"Deep learning sequence models for transcriptional regulation","volume":"25","author":"Sokolova","year":"2024","journal-title":"Annu. Rev. Genomics Hum. Genet."},{"key":"B44","doi-asserted-by":"publisher","first-page":"i266","DOI":"10.1093\/bioinformatics\/btae230","article-title":"BioCoder: a benchmark for bioinformatics code generation with large language models","volume":"40","author":"Tang","year":"2024","journal-title":"Bioinformatics"},{"key":"B45","doi-asserted-by":"publisher","first-page":"R63","DOI":"10.1093\/hmg\/ddy115","article-title":"Deep learning of genomic variation and regulatory network data","volume":"27","author":"Telenti","year":"2018","journal-title":"Hum. Mol. Genet."},{"key":"B46","doi-asserted-by":"publisher","first-page":"153351","DOI":"10.1016\/j.jplph.2020.153351","article-title":"Genomic resources in plant breeding for sustainable agriculture","volume":"257","author":"Thudi","year":"2021","journal-title":"J. Plant Physiol."},{"key":"B47","doi-asserted-by":"publisher","first-page":"e00420","DOI":"10.1128\/mbio.00420-00434","article-title":"A framework for effective application of machine learning to microbiome-based classification problems","volume":"11","author":"Top\u00e7uo\u011flu","year":"2020","journal-title":"mBio"},{"key":"B48","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s00439-018-1924-x","article-title":"Conclusion: harmonisation in genomic and health data sharing for research: an impossible dream?","volume":"137","author":"Townend","year":"2018","journal-title":"Hum. Genet"},{"key":"B49","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s12920-024-01796-9","article-title":"A systematic analysis of deep learning in genomics and histopathology for precision oncology","volume":"17","author":"Unger","year":"2024","journal-title":"BMC Med. Genomics"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1101\/2025.06.25.661622","article-title":"Genomic touchstone: benchmarking genomic language models in the context of the central dogma","author":"Wang","year":"2025","journal-title":"bioRxiv. 2025.2006.2025.661622"},{"key":"B51","doi-asserted-by":"publisher","first-page":"100780","DOI":"10.1016\/j.isci.2019.100780","article-title":"Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics","volume":"23","author":"Warnat-Herresthal","year":"2020","journal-title":"Iscience. Jan."},{"key":"B52","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1038\/s41576-021-00434-9","article-title":"Navigating the pitfalls of applying machine learning in genomics","volume":"23","author":"Whalen","year":"2022","journal-title":"Nat. Rev. Genet."},{"key":"B53","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/s00439-019-01970-5","article-title":"Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives","volume":"138","author":"Xu","year":"2019","journal-title":"Hum. Genet."},{"key":"B54","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1038\/nmeth.3547","article-title":"Predicting effects of noncoding variants with deep learning\u2013based sequence model","volume":"12","author":"Zhou","year":"2015","journal-title":"Nat. Methods"},{"key":"B55","doi-asserted-by":"publisher","first-page":"e11499","DOI":"10.2196\/11499","article-title":"Adapting state-of-the-art deep language models to clinical information extraction systems: potentials, challenges, and solutions","volume":"25","author":"Zhou","year":"2019","journal-title":"JMIR Med. Inf."}],"container-title":["Frontiers in Bioinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2026.1829576\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T05:39:24Z","timestamp":1783057164000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2026.1829576\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,3]]},"references-count":55,"alternative-id":["10.3389\/fbinf.2026.1829576"],"URL":"https:\/\/doi.org\/10.3389\/fbinf.2026.1829576","relation":{},"ISSN":["2673-7647"],"issn-type":[{"value":"2673-7647","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,3]]},"article-number":"1829576"}}