CMB_2025v15n4

Computational Molecular Biology 2025, Vol.15, No.4, 160-170 http://bioscipublisher.com/index.php/cmb 170 Habibi M., Weber L., Neves M., Wiegandt D.L., and Leser U., 2017, Deep learning with word embeddings improves biomedical named entity recognition, Bioinformatics, 33(14): i37-i48. https://doi.org/10.1093/bioinformatics/btx228 Howard J., and Ruder S., 2018, Universal language model fine-tuning for text classification, In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp.328-339. https://doi.org/10.18653/v1/P18-1031 Huang M.S., Han J.C., Lin P.Y., You Y.T., Tsai R., and Hsu W.L., 2024, Surveying biomedical relation extraction: a critical examination of current datasets and a new resource, Briefings in Bioinformatics, 25(3): bbad132. https://doi.org/10.1186/s12859-024-05749-y Ji Z., Lee N., Fries J.A., Yu T., and Finn C., 2023, Hallucination in natural language generation, ACM Computing Surveys, 55(12): 248. https://doi.org/10.1145/3571730 Kung T.H., Cheatham M., Medenilla A., Sillos C., De Leon L., Elepaño C., Madriaga M., Aggabao R., Diaz-Candido G., Maningo J., and Tseng, V., 2023, Performance of ChatGPT on USMLE: potential for AI-assisted medical education, PLOS Digital Health, 2(2): e0000198. https://doi.org/10.1371/journal.pdig.0000198 Lee J., Yoon W., Kim S., Kim D., Kim S., So C.H., and Kang J., 2020, BioBERT: a pre-trained biomedical language representation model for biomedical text mining, Bioinformatics, 36(4): 1234-1240. https://doi.org/10.1093/bioinformatics/btz682 Lewis P., Perez E., Piktus A., Petroni F., Karpukhin V., Goyal N., Küttler H., Lewis M., Yih W., Rocktäschel T., Riedel S., and Kiela D., 2020, Retrieval-augmented generation for knowledge-intensive NLP, Advances in Neural Information Processing Systems, 33: 9459-9474. Luo R., Sun L., Xia Y., Qin T., Zhang S., Poon H., and Liu T.Y., 2022, BioGPT: generative pre-trained transformer for biomedical text generation and mining, Briefings in Bioinformatics, 23(6): bbac409. https://doi.org/10.1093/bib/bbac409 Meskó B., and Topol E.J., 2023, The imperative for regulatory oversight of large language models in healthcare, NPJ Digital Medicine, 6(1): 120. https://doi.org/10.1038/s41746-023-00873-0 Moor M., Banerjee O., Abad Z.S.H., Krumholz H.M., Leskovec J., Topol E.J., and Rajpurkar P., 2023, Foundation models for generalist medical artificial intelligence, Nature, 616(7956): 259-265. https://doi.org/10.1038/s41586-023-05881-4 Nori H., King N., McKinney S.M., Carignan D., and Horvitz E., 2023, Capabilities of GPT-4 on medical challenge problems, arXiv Preprint, 2303: 13375. Ouyang L., Wu J., Jiang X., Almeida D., Wainwright C.L., Mishkin P., Zhang C., Agarwal S., Slama K., Ray A., Schulman J., Hilton J., Kelton F., Miller L., Simens M., Askell A., Welinder P., Christiano P., Leike J., and Lowe R., 2022, Training language models to follow instructions with human feedback, Advances in Neural Information Processing Systems, 35: 27730-27744. Percha B., and Altman R.B., 2018, A global network of biomedical relationships derived from text, Bioinformatics, 34(15): 2614-2624. https://doi.org/10.1093/bioinformatics/bty114 Shah N.H., Entwistle D., and Pfeffer M.A., 2023, Creation and adoption of large language models in medicine, JAMA, 330(9): 866-867. https://doi.org/10.1001/jama.2023.14217 Singhal K., Azizi S., Tu T., Mahdavi S.S., Wei J., Chung H.W., Scales N.,Tanwani A., Cole-Lewis H., Pfohl S., Payne P., Seneviratne M., Gamble P., Kelly C., Babiker A., Schärli N., Chowdhery A., Mansfield P., Demner-Fushman D., Arcas B., Webster D., Corrado G., Matias Y., Chou K., Gottweis J., Tomasev N., Liu Y., Rajkomar A., Barral J., Semturs C., Karthikesalingam A., and Natarajan V., 2023, Large language models encode clinical knowledge, Nature, 620(7972): 172-180. https://doi.org/10.1038/s41586-023-06291-2 Topol E.J., 2019, High-performance medicine: the convergence of human and artificial intelligence, Nature Medicine, 25(1): 44-56. https://doi.org/10.1038/s41591-018-0300-7 Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Kaiser Ł., and Polosukhin I., 2017, Attention is all you need, Advances in Neural Information Processing Systems, 30: 5998-6008. Wang Y., Wang L., Rastegar-Mojarad M., Moon S., Shen F., Afzal N., Liu S., Zeng Y., Mehrabi S., Sohn S., and Liu H., 2018, Clinical information extraction applications: a literature review, Journal of Biomedical Informatics, 77: 34-49. https://doi.org/10.1016/j.jbi.2017.11.011 Xu J., Kim S., Song M., Jeong M., Kim D., Kang J., Rousseau J., Li X., Xu W., Torvik V., Bu Y., Chen C., Akef Ebeid I., Li D., and Ding Y., 2020, Building a PubMed knowledge graph, Scientific Data, 7(1): 205. https://doi.org/10.1038/s41597-020-0543-2

RkJQdWJsaXNoZXIy MjQ4ODYzNA==