cirpublications.com-CJOp-1790880679-l150807572 1790880679 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2025 4 1 Ahmed Youssef Khaled Hassan Mahmoud Elamin 2025 10.68159/l150807572 https://cirpublications.com/pub/journal/1/article/l150807572 Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234-40. Alsentzer E, Murphy JR, Boag W, Weng WH, Jindi D, Naumann T, et al. Publicly available clinical BERT embeddings. In: Proc 2nd Clin Nat Lang Process Workshop. 2019. p. 72-8. Li Y, Rao S, Solares JRA, Hassaine A, Ramakrishnan R, Canoy D, et al. BEHRT: transformer for electronic health records. Sci Rep. 2020;10(1):7155. 10.1038/s41598-020-62922-y Rasmy L, Xiang Y, Xie Z, Tao C, Zhi D. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit Med. 2021;4(1):86. 10.1038/s41746-021-00455-y Yang X, Chen A, PourNejatian N, Shin HC, Smith KE, Parisien C, et al. A large language model for electronic health records. NPJ Digit Med. 2022;5(1):194. 10.1038/s41746-022-00742-2 Peng C, Yang X, Chen A, Yu Z, Smith KE, Costa AB, et al. Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need. J Am Med Inform Assoc. 2024;31(9):1892-903. Peng C, Yang XI, Smith KE, Yu Z, Chen A, Bian J, et al. Model tuning or prompt tuning? A study of large language models for clinical concept and relation extraction. J Biomed Inform. 2024;153:104630. 10.1016/j.jbi.2024.104630 Moor M, Banerjee O, Abad ZS, Krumholz HM, Leskovec J, Topol EJ, et al. Foundation models for generalist medical artificial intelligence. Nature. 2023;616(7956):259-65. 10.1038/s41586-023-05881-4 Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, et al. Large language models encode clinical knowledge. Nature. 2023;620(7972):172-80. 10.1038/s41586-023-06291-2 Sahoo SS, Plasek JM, Xu H, Uzuner Ö, Cohen T, Yetisgen M, et al. Large language models for biomedicine: foundations, opportunities, challenges, and best practices. J Am Med Inform Assoc. 2024;31(9):2114-24. Henriksson A, Pawar Y, Hedberg P, Nauclér P. Multimodal fine-tuning of clinical language models for predicting COVID-19 outcomes. Artif Intell Med. 2023;146:102695. 10.1016/j.artmed.2023.102695 Karway GK, Koyner JL, Caskey J, Spicer AB, Carey KA, Gilbert ER, et al. Development and external validation of multimodal postoperative acute kidney injury risk machine learning models. JAMIA Open. 2023;6(4):ooad109. Ding JE, Thao PN, Peng WC, Wang JZ, Chug CC, Hsieh MC, et al. Large language multimodal models for new-onset type 2 diabetes prediction using five-year cohort electronic health records. Sci Rep. 2024;14(1):20774. 10.1038/s41598-024-71320-3 Wang C, Yang X, Sun M, Gu Y, Niu J, Zhang W, et al. Multimodal fusion network for ICU patient outcome prediction. Neural Netw. 2024;180:106672. 10.1016/j.neunet.2024.106672 Chen J, Wen Y, Pokojovy M, Tseng TL, McCaffrey P, Vo A, et al. Multi-modal learning for inpatient length of stay prediction. Comput Biol Med. 2024;171:108121. 10.1016/j.compbiomed.2024.108121 Goh KH, Wang L, Yeow AY, Poh H, Li K, Yeow JJ, et al. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat Commun. 2021;12(1):711. 10.1038/s41467-021-20910-4 Lee SH. Natural language generation for electronic health records. NPJ Digit Med. 2018;1(1):63. 10.1038/s41746-018-0060-3 Tsui FR, Shi L, Ruiz V, Ryan ND, Biernesser C, Iyengar S, et al. Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts. JAMIA Open. 2021;4(1):ooab011. Percha B, Pisapati K, Gao C, Schmidt H. Natural language inference for curation of structured clinical registries from unstructured text. J Am Med Inform Assoc. 2022;29(1):97-108. Nievas M, Basu A, Wang Y, Singh H. Distilling large language models for matching patients to clinical trials. J Am Med Inform Assoc. 2024;31(9):1953-63. Yan C, Ong HH, Grabowska ME, Krantz MS, Su WC, Dickson AL, et al. Large language models facilitate the generation of electronic health record phenotyping algorithms. J Am Med Inform Assoc. 2024;31(9):1994-2001. Alamoodi AH, Zughoul O, David D, Garfan S, Pamucar D, Albahri OS, et al. A novel evaluation framework for medical LLMs: combining fuzzy logic and MCDM for medical relation and clinical concept extraction. J Med Syst. 2024;48(1):81. 10.1007/s10916-024-02067-7 Garcelon N, Burgun A, Salomon R, Neuraz A. Electronic health records for the diagnosis of rare diseases. Kidney Int. 2020;97(4):676-86. 10.1016/j.kint.2019.11.037 Lo Barco T, Kuchenbuch M, Garcelon N, Neuraz A, Nabbout R. Improving early diagnosis of rare diseases using natural language processing in unstructured medical records: an illustration from Dravet syndrome. Orphanet J Rare Dis. 2021;16(1):309. 10.1186/s13023-021-01953-4 Lo Barco T, Garcelon N, Neuraz A, Nabbout R. Natural history of rare diseases using natural language processing of narrative unstructured electronic health records: the example of Dravet syndrome. Epilepsia. 2024;65(2):350-61. 10.1111/epi.17845 Shen F, Liu S, Wang Y, Wen A, Wang L, Liu H. Utilization of electronic medical records and biomedical literature to support the diagnosis of rare diseases using data fusion and collaborative filtering approaches. JMIR Med Inform. 2018;6(4):e11301. 10.2196/11301 Jefferies JL, Spencer AK, Lau HA, Nelson MW, Giuliano JD, Zabinski JW, et al. A new approach to identifying patients with elevated risk for Fabry disease using a machine learning algorithm. Orphanet J Rare Dis. 2021;16(1):518. 10.1186/s13023-021-02129-w Yang Z, Shikany A, Ni Y, Zhang G, Weaver KN, Chen J. Using deep learning and electronic health records to detect Noonan syndrome in pediatric patients. Genet Med. 2022;24(11):2329-37. 10.1016/j.gim.2022.08.007 Herr K, Lu P, Diamreyan K, Xu H, Mendonca E, Weaver KN, et al. Estimating prevalence of rare genetic disease diagnoses using electronic health records in a children’s hospital. Hum Genet Genom Adv. 2024;5(4):100334. 10.1016/j.xhgg.2024.100334