{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:02:02Z","timestamp":1782313322142,"version":"3.54.5"},"reference-count":124,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001711","name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung","doi-asserted-by":"crossref","award":["407940-206504"],"award-info":[{"award-number":["407940-206504"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100006447","name":"Universit\u00e4t Z\u00fcrich","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006447","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-024-00197-2","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T11:38:15Z","timestamp":1734608295000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Large language models to process, analyze, and synthesize biomedical texts: a scoping review"],"prefix":"10.1007","volume":"4","author":[{"given":"Simona Emilova","family":"Doneva","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sijing","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beate","family":"Sick","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tilia","family":"Ellendorff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Philippe","family":"Goldman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerold","family":"Schneider","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin Victor","family":"Ineichen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"197_CR1","unstructured":"Zhou B, Yang G, Shi Z, Ma S. Natural language processing for smart healthcare. IEEE Reviews in Biomedical Engineering, 2022."},{"key":"197_CR2","unstructured":"Vaswani A, Shazeer N, Parmar V, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I. Attention is all you need. Adv Neural Inf Process Syst. 2017;30."},{"issue":"3","key":"197_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3611651","volume":"56","author":"B Wang","year":"2023","unstructured":"Wang B, Xie Q, Pei J, Chen Z, Tiwari P, Li Z, Jie F. Pre-trained language models in biomedical domain: a systematic survey. ACM Comput Surv. 2023;56(3):1\u201352.","journal-title":"ACM Comput Surv."},{"key":"197_CR4","unstructured":"Radford A, Narasimhan K, Salimans T, Sutskever I, et\u00a0al. Improving language understanding by generative pre-training. 2018. Preprint OpenAI."},{"key":"197_CR5","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, KaplanJared D, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, et al. Language models are few-shot learners. Adv Neural Inf Process Syst. 2020;33:1877\u2013901.","journal-title":"Adv Neural Inf Process Syst."},{"key":"197_CR6","unstructured":"Dong Q, Li L, Dai D, Zheng C, Wu Z, Chang B, Sun X, Xu J, Sui Z. A survey on in-context learning. arXiv preprint arXiv:2301.00234, 2022."},{"issue":"8","key":"197_CR7","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","volume":"29","author":"AJ Thirunavukarasu","year":"2023","unstructured":"Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med. 2023;29(8):1930\u201340.","journal-title":"Nat Med."},{"key":"197_CR8","unstructured":"Ye H, Liu T, Zhang A, Hua W, Jia W. Cognitive mirage: a review of hallucinations in large language models. arXiv preprint arXiv:2309.06794, 2023."},{"key":"197_CR9","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.tacc.2021.02.007","volume":"38","author":"S Locke","year":"2021","unstructured":"Locke S, Bashall A, Al-Adely S, Moore J, Wilson A, Kitchen Gareth B. Natural language processing in medicine: a review. Trends Anaesth Crit Care. 2021;38:4\u20139.","journal-title":"Trends Anaesth Crit Care."},{"key":"197_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2021.103982","volume":"126","author":"KS Kalyan","year":"2022","unstructured":"Kalyan KS, Rajasekharan A, Sangeetha S. AMMU: a survey of transformer-based biomedical pretrained language models. J Biomed Inf. 2022;126: 103982.","journal-title":"J Biomed Inf"},{"key":"197_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127079","volume":"568","author":"E Cesario","year":"2024","unstructured":"Cesario E, Comito C, Zumpano E. A survey of the recent trends in deep learning for literature based discovery in the biomedical domain. Neurocomputing. 2024;568: 127079.","journal-title":"Neurocomputing."},{"issue":"01","key":"197_CR12","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1055\/s-0043-1768726","volume":"32","author":"A Shaitarova","year":"2023","unstructured":"Shaitarova A, Zaghir J, Lavelli A, Krauthammer M, Rinaldi F. Exploring the latest highlights in medical natural language processing across multiple languages: a survey. Yearb Med Inform. 2023;32(01):230\u201343.","journal-title":"Yearb Med Inform."},{"key":"197_CR13","doi-asserted-by":"crossref","unstructured":"He K, Mao R, Lin Q, Ruan Y, Lan X, Feng M, Cambria E. A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics. arXiv preprint arXiv:2310.05694, 2023.","DOI":"10.2139\/ssrn.4809363"},{"issue":"6","key":"197_CR14","doi-asserted-by":"publisher","first-page":"887","DOI":"10.3390\/healthcare11060887","volume":"11","author":"M Sallam","year":"2023","unstructured":"Sallam M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare. 2023;11(6):887.","journal-title":"Healthcare"},{"issue":"4","key":"197_CR15","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1002\/hcs2.61","volume":"2","author":"R Yang","year":"2023","unstructured":"Yang R, Tan TF, Wei L, Thirunavukarasu AJ, Ting DSW, Liu N. Large language models in health care: development, applications, and challenges. Health Care Sci. 2023;2(4):255\u201363.","journal-title":"Health Care Sci"},{"issue":"2","key":"197_CR16","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1038\/s42256-020-00287-7","volume":"3","author":"R Van De Schoot","year":"2021","unstructured":"Van De Schoot R, De Bruin J, Schram R, Zahedi P, De Boer J, Weijdema F, Kramer B, Huijts M, Hoogerwerf M, Ferdinands G, et al. An open source machine learning framework for efficient and transparent systematic reviews. Nat Mach Intell. 2021;3(2):125\u201333.","journal-title":"Nat Mach Intell"},{"key":"197_CR17","unstructured":"Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems. vol. 27. Curran Associates, Inc., 2014."},{"key":"197_CR18","doi-asserted-by":"crossref","unstructured":"Cohen KB, Demner-Fushman D. Biomedical natural language processing. John Benjamins, 2014.","DOI":"10.1016\/B978-0-12-401678-1.00006-3"},{"key":"197_CR19","doi-asserted-by":"crossref","unstructured":"Luo J, Wu M, Gopukumar D, Zhao Y. Big data application in biomedical research and health care: a literature review. Biomed Inf Insights. 2016;8:BII\u2013S31559.","DOI":"10.4137\/BII.S31559"},{"key":"197_CR20","doi-asserted-by":"crossref","unstructured":"Wu J, Shi D, Hasan A, Wu H. KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization. In: Demner-fushman D, Ananiadou S, Cohen K. editors, The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pp. 535\u2013540, Toronto, Canada, July 2023. Association for Computational Linguistics.","DOI":"10.18653\/v1\/2023.bionlp-1.54"},{"key":"197_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102586","volume":"142","author":"G Cenikj","year":"2023","unstructured":"Cenikj G, Eftimov T, Seljak BK. Foodis: a food-disease relation mining pipeline. Artif Intell Med. 2023;142: 102586.","journal-title":"Artif Intell Med."},{"issue":"1","key":"197_CR22","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1186\/s12859-023-05520-9","volume":"24","author":"Joel Zirkle","year":"2023","unstructured":"Zirkle Joel, Han Xiaomei, Racz Rebecca, Samieegohar Mohammadreza, Chaturbedi Anik, Mann John, Chakravartula Shilpa, Li Zhihua. Deep learning-enabled natural language processing to identify directional pharmacokinetic drug\u2013drug interactions. BMC Bioinf. 2023;24(1):413.","journal-title":"BMC Bioinf"},{"key":"197_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-021-04504-x","volume":"23","author":"A Elangovan","year":"2022","unstructured":"Elangovan A, Li Y, Pires DEV, Davis MJ, Verspoor K. Large-scale protein\u2013protein post-translational modification extraction with distant supervision and confidence calibrated biobert. BMC Bioinf. 2022;23:1\u201323.","journal-title":"BMC Bioinf"},{"key":"197_CR24","doi-asserted-by":"crossref","unstructured":"Zuo C, Acharya N, Banerjee R. Querying across genres for medical claims in news. In: Webber B, Cohn T, He Y, Liu Y. editors. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1783\u20131789, Online, November 2020. Association for Computational Linguistics.","DOI":"10.18653\/v1\/2020.emnlp-main.139"},{"key":"197_CR25","doi-asserted-by":"crossref","unstructured":"Jiang T, Zhao T, Qin B, Liu T, Chawla N, Jiang M. Multi-input multi-output sequence labeling for joint extraction of fact and condition tuples from scientific text. In: Inui K, Jiang J, Ng V, Wan X, editors. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 302\u2013312, Hong Kong, China, November 2019. Association for Computational Linguistics.","DOI":"10.18653\/v1\/D19-1029"},{"issue":"83","key":"197_CR26","first-page":"2021","volume":"281","author":"J Mantas","year":"2021","unstructured":"Mantas J, et al. The classification of short scientific texts using pretrained BERT model. Public Health Inf Proc MIE. 2021;281(83):2021.","journal-title":"Public Health Inf Proc MIE"},{"issue":"1","key":"197_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13643-021-01763-w","volume":"10","author":"Sungmin Aum","year":"2021","unstructured":"Aum Sungmin, Choe Seon. srBERT: automatic article classification model for systematic review using bert. Syst Rev. 2021;10(1):1\u20138.","journal-title":"Syst Rev"},{"key":"197_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103578","volume":"112","author":"AK Ambalavanan","year":"2020","unstructured":"Ambalavanan AK, Devarakonda MV. Using the contextual language model BERT for multi-criteria classification of scientific articles. J Biomed Inform. 2020;112: 103578.","journal-title":"J Biomed Inform."},{"key":"197_CR29","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.reprotox.2022.09.001","volume":"113","author":"PC Habets","year":"2022","unstructured":"Habets PC, van IIjzendoorn DGP, Vinkers CH, H\u00e4rmark L, de Vries LC, Otte WM. Development and validation of a machine-learning algorithm to predict the relevance of scientific articles within the field of teratology. Reprod Toxicol. 2022;113:150\u20134.","journal-title":"Reprod Toxicol."},{"issue":"1","key":"197_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13326-023-00282-y","volume":"14","author":"AJJ Yepes","year":"2023","unstructured":"Yepes AJJ, Verspoor K. Classifying literature mentions of biological pathogens as experimentally studied using natural language processing. J Biomed Semant. 2023;14(1):1.","journal-title":"J Biomed Semant."},{"key":"197_CR31","doi-asserted-by":"crossref","unstructured":"Rosenthal S, Barker K, Liang Z. Leveraging medical literature for section prediction in electronic health records. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4864\u20134873, 2019.","DOI":"10.18653\/v1\/D19-1492"},{"issue":"1","key":"197_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-022-02085-0","volume":"22","author":"V Martenot","year":"2022","unstructured":"Martenot V, Masdeu V, Cupe J, Gehin F, Blanchon M, Dauriat J, Horst A, Renaudin M, Girard P, Zucker J-D. Lisa: an assisted literature search pipeline for detecting serious adverse drug events with deep learning. BMC Med Inform Decis Mak. 2022;22(1):1\u201316.","journal-title":"BMC Med Inform Decis Mak."},{"key":"197_CR33","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1093\/database\/baac104","volume":"baac2022","author":"\u00d6 Kart","year":"2022","unstructured":"Kart \u00d6, Mestiashvili A, Lachmann K, Kwasnicki R, Schroeder M. Emati: a recommender system for biomedical literature based on supervised learning. Database. 2022;baac2022:104.","journal-title":"Database"},{"issue":"W1","key":"197_CR34","doi-asserted-by":"publisher","first-page":"W616","DOI":"10.1093\/nar\/gkac310","volume":"50","author":"P-H Li","year":"2022","unstructured":"Li P-H, Chen T-F, Jheng-Ying Y, Shih S-H, Chan-Hung S, Lin Y-H, Tsai H-K, Juan H-F, Chen C-Y, Huang J-H. pubmedkb: an interactive web server for exploring biomedical entity relations in the biomedical literature. Nucleic Acids Res. 2022;50(W1):W616-22.","journal-title":"Nucleic Acids Res."},{"issue":"6","key":"197_CR35","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1093\/bib\/bbac409","volume":"bbac23","author":"R Luo","year":"2022","unstructured":"Luo R, Sun L, Xia Y, Qin T, Zhang S, Poon H, Liu TY. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief Bioinf. 2022;bbac23(6):409.","journal-title":"Brief Bioinf"},{"key":"197_CR36","doi-asserted-by":"crossref","unstructured":"Yiqi T, Yidong C, Xiaodong S. A multi-task approach for improving biomedical named entity recognition by incorporating multi-granularity information. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP. 2021;2021:4804\u201313.","DOI":"10.18653\/v1\/2021.findings-acl.424"},{"key":"197_CR37","doi-asserted-by":"crossref","unstructured":"Lu Q, Dou D, Nguyen TH. Parameter-efficient domain knowledge integration from multiple sources for biomedical pre-trained language models. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 3855\u20133865, 2021.","DOI":"10.18653\/v1\/2021.findings-emnlp.325"},{"issue":"1","key":"197_CR38","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1093\/bioinformatics\/btac754","volume":"btac39","author":"M Asada","year":"2023","unstructured":"Asada M, Miwa M, Sasaki Y. Integrating heterogeneous knowledge graphs into drug-drug interaction extraction from the literature. Bioinformatics. 2023;btac39(1):754.","journal-title":"Bioinformatics"},{"key":"197_CR39","unstructured":"Guan H, Devarakonda M. Leveraging contextual information in extracting long distance relations from clinical notes. In: AMIA Annual Symposium Proceedings, volume 2019, page 1051. American Medical Informatics Association, 2019."},{"key":"197_CR40","doi-asserted-by":"crossref","unstructured":"Hussain S, Afzal H, Saeed R, Iltaf N, Umair MY. Pharmacovigilance with transformers: a framework to detect adverse drug reactions using BERT fine-tuned with farm. Comput Math Methods Med. 2021; 2021.","DOI":"10.1155\/2021\/5589829"},{"key":"197_CR41","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhou B, Song K, Sui X, Zhao G, Jiang N, Yuan X. PM2F2N: patient multi-view multi-modal feature fusion networks for clinical outcome prediction. In: Findings of the Association for Computational Linguistics: EMNLP. 2022;2022:1985\u201394.","DOI":"10.18653\/v1\/2022.findings-emnlp.144"},{"key":"197_CR42","doi-asserted-by":"crossref","unstructured":"Deznabi I, Iyyer M, Fiterau M. Predicting in-hospital mortality by combining clinical notes with time-series data. In: Findings of the association for computational linguistics: ACL-IJCNLP. 2021;2021:4026\u201331.","DOI":"10.18653\/v1\/2021.findings-acl.352"},{"key":"197_CR43","doi-asserted-by":"crossref","unstructured":"Duan J, Wei F, Liu J, Li H, Liu T, Wang J. CDA: a contrastive data augmentation method for alzheimer\u00e2\u20ac\u2122s disease detection. In: Findings of the Association for Computational Linguistics: ACL. 2023;2023:1819\u201326.","DOI":"10.18653\/v1\/2023.findings-acl.114"},{"key":"197_CR44","doi-asserted-by":"crossref","unstructured":"Aich A, Quynh A, Badal V, Pinkham A, Harvey P, Depp C, Parde N. Towards intelligent clinically-informed language analyses of people with bipolar disorder and schizophrenia. In: Findings of the Association for Computational Linguistics: EMNLP. 2022;2022:2871\u201387.","DOI":"10.18653\/v1\/2022.findings-emnlp.208"},{"key":"197_CR45","doi-asserted-by":"crossref","unstructured":"Sourabh Z, Xiaofei L, Wiechmann D, Qiao Y, Kerz E. What to fuse and how to fuse: Exploring emotion and personality fusion strategies for explainable mental disorder detection. In: Findings of the Association for Computational Linguistics: ACL. 2023;2023:8926\u201340.","DOI":"10.18653\/v1\/2023.findings-acl.568"},{"key":"197_CR46","doi-asserted-by":"crossref","unstructured":"Sawhney R, Neerkaje AT, Gaur M. A risk-averse mechanism for suicidality assessment on social media. In: Association for Computational Linguistics 2022 (ACL 2022), 2022.","DOI":"10.18653\/v1\/2022.acl-short.70"},{"key":"197_CR47","doi-asserted-by":"crossref","unstructured":"Sawhney R, Joshi H, Gandhi S, Shah R. A time-aware transformer based model for suicide ideation detection on social media. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp. 7685\u20137697, 2020.","DOI":"10.18653\/v1\/2020.emnlp-main.619"},{"key":"197_CR48","doi-asserted-by":"crossref","unstructured":"Sosea T, Caragea C. Canceremo: a dataset for fine-grained emotion detection. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 8892\u20138904, 2020.","DOI":"10.18653\/v1\/2020.emnlp-main.715"},{"key":"197_CR49","doi-asserted-by":"crossref","unstructured":"Hossain T, Logan Iv RL, Ugarte A, Matsubara Y, Young S, Singh S. Covidlies: Detecting covid-19 misinformation on social media. In: Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, 2020.","DOI":"10.18653\/v1\/2020.nlpcovid19-2.11"},{"key":"197_CR50","doi-asserted-by":"crossref","unstructured":"Liu Z, Xiong C, Dai Z, Sun S, Sun M, Liu Z. Adapting open domain fact extraction and verification to COVID-FACT through in-domain language modeling. In: Findings of the Association for Computational Linguistics: EMNLP. 2020;2020:2395\u2013400.","DOI":"10.18653\/v1\/2020.findings-emnlp.216"},{"key":"197_CR51","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018."},{"issue":"1","key":"197_CR52","first-page":"1","volume":"3","author":"G Yu","year":"2021","unstructured":"Yu G, Tinn R, Cheng H, Lucas M, Usuyama N, Liu X, Naumann T, Gao J, Poon H. Domain-specific language model pretraining for biomedical natural language processing. ACM Trans Comput Healthc (HEALTH). 2021;3(1):1\u201323.","journal-title":"ACM Trans Comput Healthc (HEALTH)."},{"issue":"4","key":"197_CR53","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, Kang J. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234\u201340.","journal-title":"Bioinformatics."},{"key":"197_CR54","doi-asserted-by":"crossref","unstructured":"Beltagy I, Lo K, Cohan A. SciBERT: a pretrained language model for scientific text. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3615\u20133620, Hong Kong, China, November 2019. Association for Computational Linguistics.","DOI":"10.18653\/v1\/D19-1371"},{"key":"197_CR55","doi-asserted-by":"crossref","unstructured":"Peng Y, Shankai Y, Zhiyong L. Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets. In: Proceedings of the 2019 Workshop on Biomedical Natural Language Processing (BioNLP 2019), pp. 58\u201365, 2019.","DOI":"10.18653\/v1\/W19-5006"},{"key":"197_CR56","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1093\/database\/baz116","volume":"baz2019","author":"T Chen","year":"2019","unstructured":"Chen T, Wu M, Li H. A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning. Database. 2019;baz2019:116.","journal-title":"Database"},{"key":"197_CR57","unstructured":"Tang B, Jiang D, Chen Q, Wang X, Yan J, Shen Y. De-identification of clinical text via Bi-LSTM-CRF with neural language models. In AMIA Annual Symposium Proceedings. vol. 2019, pp. 857. American Medical Informatics Association, 2019."},{"issue":"11","key":"197_CR58","doi-asserted-by":"publisher","DOI":"10.2196\/22508","volume":"8","author":"D Mahajan","year":"2020","unstructured":"Mahajan D, Poddar A, Liang JJ, Lin Y-T, Prager JM, Suryanarayanan P, Raghavan P, Tsou C-H, et al. Identification of semantically similar sentences in clinical notes: iterative intermediate training using multi-task learning. JMIR Med Inf. 2020;8(11): e22508.","journal-title":"JMIR Med Inf"},{"key":"197_CR59","doi-asserted-by":"crossref","unstructured":"Wang H, Ma F, Wang Y, Gao J. Knowledge-guided paraphrase identification. In: Findings of the Association for Computational Linguistics: EMNLP. 2021;2021:843\u201353.","DOI":"10.18653\/v1\/2021.findings-emnlp.72"},{"key":"197_CR60","doi-asserted-by":"crossref","unstructured":"Xiong Y, Yang X, Liu L, Wong K-C, Chen Q, Xiang Y, Tang B. EARA: improving Biomedical Semantic Textual Similarity with Entity-Aligned Attention and Retrieval Augmentation. In: The 2023 Conference on Empirical Methods in Natural Language Processing, 2023.","DOI":"10.18653\/v1\/2023.findings-emnlp.586"},{"key":"197_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2023.104370","volume":"142","author":"A Chen","year":"2023","unstructured":"Chen A, Zehao Y, Yang X, Guo Y, Bian J, Yonghui W. Contextualized medication information extraction using transformer-based deep learning architectures. J Biomed Inform. 2023;142: 104370.","journal-title":"J Biomed Inform."},{"key":"197_CR62","doi-asserted-by":"publisher","first-page":"baac056","DOI":"10.1093\/database\/baac056","volume":"2022","author":"S-J Lin","year":"2022","unstructured":"Lin S-J, Yeh W-C, Chiu Y-W, Chang Y-C, Hsu M-H, Chen Y-S, Hsu W-L. A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in pubmed articles. Database. 2022;2022:baac056.","journal-title":"Database"},{"issue":"2","key":"197_CR63","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1007\/s10844-022-00768-8","volume":"60","author":"G Rabby","year":"2023","unstructured":"Rabby G, Berka P. Multi-class classification of COVID-19 documents using machine learning algorithms. J Intell Inf Syst. 2023;60(2):571\u201391.","journal-title":"J Intell Inf Syst"},{"key":"197_CR64","doi-asserted-by":"crossref","unstructured":"Aldahdooh J, Tanoli Z, Tang J. Mining drug-target interactions from biomedical literature using chemical and gene descriptions based ensemble transformer model. bioRxiv. 2023\u201307, 2023.","DOI":"10.1101\/2023.07.24.550359"},{"key":"197_CR65","doi-asserted-by":"crossref","unstructured":"Sarrouti M, Abacha AB, M\u00e2\u20ac\u2122rabet Y, Demner-Fushman D. Evidence-based fact-checking of health-related claims. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 3499\u20133512, 2021.","DOI":"10.18653\/v1\/2021.findings-emnlp.297"},{"key":"197_CR66","doi-asserted-by":"crossref","unstructured":"Almeida T, Matos S. Frugal neural reranking: evaluation on the covid-19 literature. In: Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, 2020.","DOI":"10.18653\/v1\/2020.nlpcovid19-2.3"},{"key":"197_CR67","doi-asserted-by":"crossref","unstructured":"Tahri C, Bochnakian A, Haouat P, Tannier X. Transitioning from benchmarks to a real-world case of information-seeking in scientific publications. In: Findings of the Association for Computational Linguistics: ACL 2023, pp. 1066\u20131076. Association for Computational Linguistics, 2023.","DOI":"10.18653\/v1\/2023.findings-acl.68"},{"key":"197_CR68","doi-asserted-by":"crossref","unstructured":"Otegi A, Campos JA, Azkune G, Soroa A, Agirre E. Automatic evaluation vs. user preference in neural textual questionanswering over COVID-19 scientific literature. In: Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, 2020.","DOI":"10.18653\/v1\/2020.nlpcovid19-2.15"},{"issue":"1","key":"197_CR69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-022-05124-9","volume":"24","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Lin X, Shanshan W. A hybrid algorithm for clinical decision support in precision medicine based on machine learning. BMC Bioinf. 2023;24(1):1\u201318.","journal-title":"BMC Bioinf"},{"issue":"1","key":"197_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-022-01862-1","volume":"22","author":"Y Li","year":"2022","unstructured":"Li Y, Zhou X, Ma J, Ma X, Cheng P, Gong T, Li C. Distinguished representation of identical mentions in bio-entity coreference resolution. BMC Med Inform Decis Mak. 2022;22(1):1\u201312.","journal-title":"BMC Med Inform Decis Mak."},{"key":"197_CR71","doi-asserted-by":"crossref","unstructured":"Zeng Q, Wenhao Y, Mengxia Y, Jiang T, Weninger T, Jiang M. Tri-train: automatic pre-fine tuning between pre-training and fine-tuning for SciNER. In: Findings of the Association for Computational Linguistics: EMNLP. 2020;2020:4778\u201387.","DOI":"10.18653\/v1\/2020.findings-emnlp.429"},{"key":"197_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-017-0459-5","volume":"17","author":"G Li","year":"2017","unstructured":"Li G, Abbade LPF, Nwosu I, Jin Y, Leenus A, Maaz M, Wang M, Bhatt M, Zielinski L, Sanger N, et al. A scoping review of comparisons between abstracts and full reports in primary biomedical research. BMC Med Res Methodol. 2017;17:1\u201312.","journal-title":"BMC Med Res Methodol"},{"key":"197_CR73","first-page":"1","volume":"11","author":"K Bretonnel Cohen","year":"2010","unstructured":"Bretonnel Cohen K, Johnson HL, Verspoor K, Roeder C, Hunter LE. The structural and content aspects of abstracts versus bodies of full text journal articles are different. BMC Bioinf. 2010;11:1\u201310.","journal-title":"BMC Bioinf"},{"key":"197_CR74","first-page":"269","volume":"2020","author":"Z Ji","year":"2020","unstructured":"Ji Z, Wei Q, Hua X. Bert-based ranking for biomedical entity normalization. AMIA Summits Transl Sci Proc. 2020;2020:269.","journal-title":"AMIA Summits Transl Sci Proc."},{"issue":"23","key":"197_CR75","doi-asserted-by":"publisher","first-page":"14934","DOI":"10.3390\/ijms232314934","volume":"23","author":"TV Ivanisenko","year":"2022","unstructured":"Ivanisenko TV, Demenkov PS, Kolchanov NA, Ivanisenko VA. The new version of the ANDDigest tool with improved ai-based short names recognition. Int J Mol Sci. 2022;23(23):14934.","journal-title":"Int J Mol Sci."},{"issue":"1","key":"197_CR76","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-022-04688-w","volume":"23","author":"U Naseem","year":"2022","unstructured":"Naseem U, Dunn AG, Khushi M, Kim J. Benchmarking for biomedical natural language processing tasks with a domain specific albert. BMC Bioinf. 2022;23(1):1\u201315.","journal-title":"BMC Bioinf"},{"issue":"11","key":"197_CR77","first-page":"2215","volume":"66","author":"L Bornmann","year":"2015","unstructured":"Bornmann L, Mutz R. Growth rates of modern science: a bibliometric analysis based on the number of publications and cited references. J Am Soc Inf Sci. 2015;66(11):2215\u201322.","journal-title":"J Am Soc Inf Sci."},{"issue":"10","key":"197_CR78","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1038\/s41684-023-01256-4","volume":"52","author":"BV Ineichen","year":"2023","unstructured":"Ineichen BV, Rosso M, Macleod MR. From data deluge to publomics: how AI can transform animal research. Lab Anim. 2023;52(10):213\u20134.","journal-title":"Lab Anim."},{"key":"197_CR79","doi-asserted-by":"crossref","unstructured":"Khraisha Q, Put S, Kappenberg J, Warraitch A, Hadfield K. Can large language models replace humans in the systematic review process? evaluating GPT-4\u2019s efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages. arXiv preprint arXiv:2310.17526, 2023.","DOI":"10.1002\/jrsm.1715"},{"issue":"1","key":"197_CR80","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1038\/s41746-023-00896-7","volume":"6","author":"L Tang","year":"2023","unstructured":"Tang L, Sun Z, Idnay B, Nestor JG, Soroush A, Elias PA, Xu Z, Ding Y, Durrett G, Rousseau JF, et al. Evaluating large language models on medical evidence summarization. NPJ Digital Med. 2023;6(1):158.","journal-title":"NPJ Digital Med"},{"key":"197_CR81","doi-asserted-by":"publisher","first-page":"1258887","DOI":"10.3389\/fpsyt.2023.1258887","volume":"14","author":"BG Patra","year":"2023","unstructured":"Patra BG, Sun Z, Cheng Z, Joly R, Pathak J, Peng Y. Automated classification of lay health articles using natural language processing: a case study on pregnancy health and postpartum depression. Front Psychiatry. 2023;14:1258887.","journal-title":"Front Psychiatry"},{"key":"197_CR82","doi-asserted-by":"publisher","first-page":"1238140","DOI":"10.3389\/fgene.2023.1238140","volume":"14","author":"I Stepanov","year":"2023","unstructured":"Stepanov I, Ivasiuk A, Yavorskyi O, Frolova A. Comparative analysis of classification techniques for topic-based biomedical literature categorisation. Front Genet. 2023;14:1238140.","journal-title":"Front Genet."},{"issue":"1","key":"197_CR83","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1186\/s12859-023-05411-z","volume":"24","author":"N Karkera","year":"2023","unstructured":"Karkera N, Acharya S, Palaniappan SK. Leveraging pre-trained language models for mining microbiome-disease relationships. BMC Bioinf. 2023;24(1):290.","journal-title":"BMC Bioinf."},{"key":"197_CR84","doi-asserted-by":"crossref","unstructured":"Jimenez\u00a0Gutierrez B, McNeal N, Washington C, Chen Y, Li L, Sun H, Su Y. Thinking about GPT-3 in-context learning for biomedical IE? think again. In: Goldberg Y, Kozareva Z, Zhang Y. editors, Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 4497\u20134512, Abu Dhabi, United Arab Emirates, December 2022. Association for Computational Linguistics.","DOI":"10.18653\/v1\/2022.findings-emnlp.329"},{"issue":"9","key":"197_CR85","doi-asserted-by":"publisher","first-page":"btad557","DOI":"10.1093\/bioinformatics\/btad557","volume":"39","author":"Q Chen","year":"2023","unstructured":"Chen Q, Sun H, Liu H, Jiang Y, Ran T, Jin X, Xiao X, Lin Z, Chen H, Niu Z. An extensive benchmark study on biomedical text generation and mining with ChatGPT. Bioinformatics. 2023;39(9):btad557.","journal-title":"Bioinformatics"},{"key":"197_CR86","doi-asserted-by":"crossref","unstructured":"Bousselham H, Nfaoui EH, Mourhir A. Fine-tuning gpt on biomedical nlp tasks: An empirical evaluation. In: 2024 International Conference on Computer, Electrical & Communication Engineering (ICCECE), pp. 2024;1\u20136.","DOI":"10.1109\/ICCECE58645.2024.10497313"},{"key":"197_CR87","doi-asserted-by":"crossref","unstructured":"Ruixi L, Hwee TN. Mind the biases: quantifying cognitive biases in language model prompting. In: Findings of the Association for Computational Linguistics: ACL. 2023;2023:5269\u201381.","DOI":"10.18653\/v1\/2023.findings-acl.324"},{"key":"197_CR88","unstructured":"Wang B, Chen W, Pei H, Xie C, Kang M, Zhang C, Xu C, Xiong Z, Dutta R, Schaeffer R, et\u00a0al. DecodingTrust: a comprehensive assessment of trustworthiness in gpt models. arXiv preprint arXiv:2306.11698, 2023."},{"issue":"1","key":"197_CR89","doi-asserted-by":"publisher","first-page":"bbad493","DOI":"10.1093\/bib\/bbad493","volume":"25","author":"S Tian","year":"2024","unstructured":"Tian S, Jin Q, Yeganova L, Lai P-T, Zhu Q, Chen X, Yang Y, Chen Q, Kim W, Comeau DC, et al. Opportunities and challenges for ChatGPT and large language models in biomedicine and health. Brief Bioinf. 2024;25(1):bbad493.","journal-title":"Brief Bioinf"},{"key":"197_CR90","doi-asserted-by":"crossref","unstructured":"Goldacre B, Morton CE, DeVito NJ. Why researchers should share their analytic code, 2019.","DOI":"10.1136\/bmj.l6365"},{"key":"197_CR91","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.9924","volume":"8","author":"J Kim","year":"2020","unstructured":"Kim J, Kim S, Cho H-M, Chang JH, Kim SY. Data sharing policies of journals in life, health, and physical sciences indexed in journal citation reports. PeerJ. 2020;8: e9924.","journal-title":"PeerJ"},{"key":"197_CR92","doi-asserted-by":"crossref","unstructured":"Dodge J, Gururangan S, Card D, Schwartz R, Smith NA. Show your work: improved reporting of experimental results. arXiv preprint arXiv:1909.03004, 2019.","DOI":"10.18653\/v1\/D19-1224"},{"key":"197_CR93","unstructured":"Kapoor S, Cantrell E, Peng K, Pham TH, Bail CA, Gundersen OE, Hofman JM, Hullman J, Lones MA, Malik MM, et\u00a0al. Reforms: Reporting standards for machine learning based science. arXiv preprint arXiv:2308.07832, 2023."},{"key":"197_CR94","doi-asserted-by":"crossref","unstructured":"Magnusson I, Smith NA, Dodge J. Reproducibility in nlp: What have we learned from the checklist? arXiv preprint arXiv:2306.09562, 2023.","DOI":"10.18653\/v1\/2023.findings-acl.809"},{"key":"197_CR95","doi-asserted-by":"crossref","unstructured":"Liesenfeld A, Lopez A, Dingemanse M. Opening up ChatGPT: racking openness, transparency, and accountability in instruction-tuned text generators. In: Proceedings of the 5th international conference on conversational user interfaces, pp. 1\u20136, 2023.","DOI":"10.1145\/3571884.3604316"},{"key":"197_CR96","unstructured":"Touvron H, Martin L, Stone K, Albert P, Almahairi A, Babaei Y, Bashlykov N, Batra S, Bhargava P, Bhosale S, et\u00a0al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023."},{"key":"197_CR97","unstructured":"Chen Z, Cano AH, Romanou A, Bonnet A, Matoba K, Salvi F, Pagliardini M, Fan S, K\u00f6pf A, Mohtashami, A et\u00a0al. Meditron-70b: scaling medical pretraining for large language models. arXiv preprint arXiv:2311.16079, 2023."},{"key":"197_CR98","doi-asserted-by":"crossref","unstructured":"Labrak Y, Bazoge A, Morin E, Gourraud P-A, Rouvier M, Dufour R. BioMistral: a collection of open-source pretrained large language models for medical domains. arXiv preprint arXiv:2402.10373, 2024.","DOI":"10.18653\/v1\/2024.findings-acl.348"},{"issue":"7956","key":"197_CR99","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","volume":"616","author":"M Moor","year":"2023","unstructured":"Moor M, Banerjee O, Abad ZSH, Krumholz HM, Leskovec J, Topol EJ, Rajpurkar P. Foundation models for generalist medical artificial intelligence. Nature. 2023;616(7956):259\u201365.","journal-title":"Nature."},{"issue":"1","key":"197_CR100","doi-asserted-by":"publisher","first-page":"bbad493","DOI":"10.1093\/bib\/bbad493","volume":"25","author":"S Tian","year":"2024","unstructured":"Tian S, Jin Q, Yeganova L, Lai P-T, Zhu Q, Chen X, Yang Y, Chen Q, Kim W, Comeau DC, Islamaj R, Kapoor A, Gao X, Lu Z. Opportunities and challenges for ChatGPT and large language models in biomedicine and health. Brief Bioinf. 2024;25(1):bbad493.","journal-title":"Brief Bioinf"},{"key":"197_CR101","doi-asserted-by":"crossref","unstructured":"Tu T, Azizi S, Driess D, Schaekermann M, Amin M, Chang P-C, Carroll A, Lau C, Tanno R, Ktena I, Palepu A, Mustafa B, Chowdhery A, Liu Y, Kornblith S, Fleet D, Mansfield P, Prakash S, Wong R, Virmani S, Semturs C, Mahdavi SS, Green B, Dominowska E, Arcas BA, Barral J, Webster D, Corrado GS, Matias Y, Singhal K, Florence P, Karthikesalingam A, Natarajan V. Towards Generalist Biomedical AI. NEJM AI, 1(3):AIoa2300138, February 2024. Massachusetts Medical Society.","DOI":"10.1056\/AIoa2300138"},{"issue":"9","key":"197_CR102","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1038\/s41591-022-01981-2","volume":"28","author":"JAN Acosta","year":"2022","unstructured":"Acosta JAN, Falcone GJ, Rajpurkar P, Topol EJ. Multimodal biomedical AI. Nat Med. 2022;28(9):1773\u201384.","journal-title":"Nat Med"},{"key":"197_CR103","unstructured":"Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, Krueger G, Sutskever I. Learning Transferable Visual Models From Natural Language Supervision. In: Proceedings of the 38th International Conference on Machine Learning, pages 8748\u20138763. PMLR, July 2021. ISSN: 2640-3498."},{"key":"197_CR104","doi-asserted-by":"crossref","unstructured":"Kim B, Nakashole N. SYMPTOMIFY: Transforming symptom annotations with language model knowledge harvesting. In: Findings of the Association for Computational Linguistics: EMNLP. 2023;2023:11667\u201381.","DOI":"10.18653\/v1\/2023.findings-emnlp.781"},{"key":"197_CR105","doi-asserted-by":"crossref","unstructured":"Wu J, Shi D, Hasan A, Wu H. KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 535\u2013540. ACL, 2023.","DOI":"10.18653\/v1\/2023.bionlp-1.54"},{"issue":"3","key":"197_CR106","doi-asserted-by":"publisher","first-page":"e240357","DOI":"10.1001\/jamanetworkopen.2024.0357","volume":"7","author":"J Zaretsky","year":"2024","unstructured":"Zaretsky J, Kim JM, Baskharoun S, Zhao Y, Austrian J, Aphinyanaphongs Y, Gupta R, Blecker SB, Feldman J. Generative artificial intelligence to transform inpatient discharge summaries to patient-friendly language and format. JAMA Netw Open. 2024;7(3):e240357\u2013e240357.","journal-title":"JAMA Netw Open."},{"issue":"2","key":"197_CR107","doi-asserted-by":"publisher","first-page":"605","DOI":"10.12669\/pjms.39.2.7653","volume":"39","author":"RA Khan","year":"2023","unstructured":"Khan RA, Jawaid M, Khan AR, Sajjad M. ChatGPT\u2014reshaping medical education and clinical management. Pak J Med Sci. 2023;39(2):605\u20137.","journal-title":"Pak J Med Sci"},{"issue":"6","key":"197_CR108","doi-asserted-by":"publisher","first-page":"1496","DOI":"10.1016\/j.surg.2024.02.019","volume":"175","author":"S Rodler","year":"2024","unstructured":"Rodler S, Ganjavi C, De Backer P, Magoulianitis V, Ramacciotti LS, De Castro Abreu AL, Gill IS, Cacciamani GE. Generative artificial intelligence in surgery. Surgery. 2024;175(6):1496\u2013502.","journal-title":"Surgery"},{"issue":"n71","key":"197_CR109","first-page":"3","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (Clin Res Ed). 2021;372(n71):3.","journal-title":"BMJ (Clin Res Ed)"},{"key":"197_CR110","unstructured":"Yang J, Jin H, Tang R, Han X, Feng Q, Jiang H, Yin B, Hu X. Harnessing the power of llms in practice: a survey on chatgpt and beyond. arXiv preprint arXiv:2304.13712, 2023."},{"issue":"1","key":"197_CR111","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AEW Johnson","year":"2016","unstructured":"Johnson AEW, Pollard TJ, Shen L, Lehman LH, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3(1):1\u20139.","journal-title":"Sci Data"},{"issue":"6","key":"197_CR112","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1016\/j.fmre.2021.11.011","volume":"1","author":"M Zhang","year":"2021","unstructured":"Zhang M, Li J. A commentary of GPT-3 in MIT technology review 2021. Fundament Res. 2021;1(6):831\u20133.","journal-title":"Fundament Res."},{"key":"197_CR113","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jbi.2013.12.006","volume":"47","author":"RI Do\u011fan","year":"2014","unstructured":"Do\u011fan RI, Leaman R, Lu Z. NCBI disease corpus: a resource for disease name recognition and concept normalization. J Biomed Inf. 2014;47:1\u201310.","journal-title":"J Biomed Inf"},{"key":"197_CR114","doi-asserted-by":"crossref","unstructured":"Li J, Sun Y, Johnson RJ, Sciaky D, Wei C-H, Leaman R, Davis AP, Mattingly CJ, Wiegers TC, Lu Z. BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database. 2016.","DOI":"10.1093\/database\/baw068"},{"key":"197_CR115","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/gb-2008-9-s2-s2","volume":"9","author":"L Smith","year":"2008","unstructured":"Smith L, Tanabe LK, JohnsonneeAndo R, Kuo C-J, Chung I-F, Hsu C-N, Lin Y-S, Klinger R, Friedrich CM, Ganchev K, et al. Overview of BioCreative II gene mention recognition. Genome Biol. 2008;9:1\u201319.","journal-title":"Genome Biol"},{"key":"197_CR116","unstructured":"Collier N, Ohta T, Tsuruoka Y, Tateisi Y, Kim J-D. Introduction to the bio-entity recognition task at JNLPBA. In: Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA\/BioNLP), pp. 2004;73\u201378."},{"key":"197_CR117","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1758-2946-7-S1-S1","volume":"7","author":"M Krallinger","year":"2015","unstructured":"Krallinger M, Leitner F, Rabal O, Vazquez M, Oyarzabal J, Valencia A. Chemdner: the drugs and chemical names extraction challenge. J Cheminf. 2015;7:1\u201311.","journal-title":"J Cheminf"},{"key":"197_CR118","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-11-85","volume":"11","author":"M Gerner","year":"2010","unstructured":"Gerner M, Nenadic G, Bergman CM. Linnaeus: a species name identification system for biomedical literature. BMC Bioinf. 2010;11:1\u201317.","journal-title":"BMC Bioinf"},{"issue":"6","key":"197_CR119","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0065390","volume":"8","author":"E Pafilis","year":"2013","unstructured":"Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, Vasileiadou A, Arvanitidis C, Jensen LJ. The species and organisms resources for fast and accurate identification of taxonomic names in text. PloS One. 2013;8(6): e65390.","journal-title":"PloS One"},{"key":"197_CR120","unstructured":"Krallinger M, Rabal O, Akhondi SA, P\u00e9rez MP, Santamar\u00eda J, Rodr\u00edguez GP, Tsatsaronis G, Intxaurrondo A, L\u00f3pez JA, Nandal U, et\u00a0al. Overview of the BioCreative VI chemical-protein interaction track. In: Proceedings of the sixth BioCreative challenge evaluation workshop, vol.\u00a01, pp. 2017;141\u2013146."},{"issue":"5","key":"197_CR121","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.jbi.2013.07.011","volume":"46","author":"M Herrero-Zazo","year":"2013","unstructured":"Herrero-Zazo M, Segura-Bedmar I, Mart\u00ednez P, Declerck T. The ddi corpus: an annotated corpus with pharmacological substances and drug\u2013drug interactions. J Biomed Inf. 2013;46(5):914\u201320.","journal-title":"J Biomed Inf."},{"key":"197_CR122","doi-asserted-by":"crossref","unstructured":"Nentidis A, Bougiatiotis K, Krithara A, Paliouras G. Results of the seventh edition of the bioasq challenge. In: Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, W\u00fcrzburg, Germany, September 16\u201320, 2019, Proceedings, Part II, pp. 553\u2013568. Springer, 2020.","DOI":"10.1007\/978-3-030-43887-6_51"},{"issue":"5","key":"197_CR123","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1016\/j.jbi.2012.04.004","volume":"45","author":"EM Van Mulligen","year":"2012","unstructured":"Van Mulligen EM, Fourrier-Reglat A, Gurwitz D, Molokhia M, Nieto A, Trifiro G, Kors JA, Furlong LI. The EU-ADR corpus: annotated drugs, diseases, targets, and their relationships. J Biomed Inf. 2012;45(5):879\u201384.","journal-title":"J Biomed Inf."},{"issue":"5","key":"197_CR124","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1136\/amiajnl-2011-000203","volume":"18","author":"\u00d6 Uzuner","year":"2011","unstructured":"Uzuner \u00d6, South BR, Shen S, DuVall SL. 2010 i2b2\/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inf Assoc. 2011;18(5):552\u20136.","journal-title":"J Am Med Inf Assoc"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-024-00197-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-024-00197-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-024-00197-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T12:26:12Z","timestamp":1734611172000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-024-00197-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,19]]},"references-count":124,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["197"],"URL":"https:\/\/doi.org\/10.1007\/s44163-024-00197-2","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,19]]},"assertion":[{"value":"7 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interest related to this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"107"}}