eISSN 2097-6046
ISSN 2096-7446
CN 10-1655/R
Responsible Institution:China Association for Science and Technology
Sponsor:Chinese Nursing Association
Review

The application of large language models in emergency and critical care nursing

  • ZHEN Haiyan ,
  • QIN Zilan ,
  • ZHANG Zhigang ,
  • WU Yuchen ,
  • YUE Weigang ,
  • AN Yuyan
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  • 1 Department of General SurgeryLanzhou University First HospitalLanzhou 730011, China
    2 Department of Critical Care MedicineLanzhou University First HospitalLanzhou 730011, China

Received date: 2025-07-15

  Online published: 2026-04-28

Supported by

Intramural Fund of the First Hospital of Lanzhou University(ldyyyn2020-39)

Abstract

With the continuous advancement of interdisciplinary research in medical engineering,large language models have demonstrated innovative potential in the medical field. In emergency and critical care clinical nursing,the integration of large language models has shown remarkable efficacy in disease diagnosis assistance,clinical nursing decision support,medical documentation automation,and patient health guidance and consultation. This paper systematically reviews the technological evolution of large language models,focusing on their current applications in emergency and critical care medicine. It also conducts an in-depth analysis of challenges in incorporating large language models into healthcare systems,including data privacy and security,legal and ethical considerations and model reliability,aiming to provide a reference for the standardized implementation of artificial intelligence technology in emergency and critical care nursing.

Cite this article

ZHEN Haiyan , QIN Zilan , ZHANG Zhigang , WU Yuchen , YUE Weigang , AN Yuyan . The application of large language models in emergency and critical care nursing[J]. Chinese Journal of Emergency and Critical Care Nursing, 2026 , 7(5) : 636 -640 . DOI: 10.3761/j.issn.2096-7446.2026.05.020

References

[1] Yin JM, Ngiam KY, Teo HH. Role of artificial intelligence ap-plications in real-life clinical practice:systematic review[J]. J Med Internet Res, 2021, 23(4):e25759.
[2] Bedi S, Liu YT, Orr-Ewing L, et al. Testing and evaluation of health care applications of large language models:a systematic review[J]. JAMA, 2025, 333(4):319-328.
[3] Omiye JA, Gui HW, Rezaei SJ, et al. Large language models in medicine:the potentials and pitfalls:a narrative review[J]. Ann Intern Med, 2024, 177(2):210-220.
[4] Nijor S, Rallis G, Lad N, et al. Patient safety issues from information overload in electronic medical records[J]. J Patient Saf, 2022, 18(6):e999-e1003.
[5] Gallifant J, Fiske A, Levites Strekalova YA, et al. Peer review of GPT-4 technical report and systems card[J]. PLoS Digit Health, 2024, 3(1):e0000417.
[6] Ray PP. ChatGPT:a comprehensive review on background,ap-plications,key challenges,bias,ethics,limitations and future sco-pe[J]. Internet Things Cyber Phys Syst, 2023, 3:121-154.
[7] Thirunavukarasu AJ, Ting DSJ, Elangovan K, et al. Large lan-guage models in medicine[J]. Nat Med, 2023, 29(8):1930-1940.
[8] 张林英. 基于人工智能技术的泌尿外科临床护理中应用研究与进展[J]. 中国急救复苏与灾害医学杂志, 2025, 20(10):1387-1390,1398.
  Zhang LY. Research and progress on the application of AI technology in clinical nursing of urology[J]. China J Emerg Re-susc Disaster Med, 2025, 20(10):1387-1390,1398.
[9] Abdullahi T, Singh R, Eickhoff C. Learning to make rare and complex diagnoses with generative AI assistance:qualitative study of popular large language models[J]. JMIR Med Educ, 2024, 10:e51391.
[10] Burdick H, Pino E, Gabel-Comeau D, et al. Validation of a machine learning algorithm for early severe sepsis prediction:a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals[J]. BMC Med Inform Decis Mak, 2020, 20(1):276.
[11] Henry KE, Adams R, Parent C, et al. Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing[J]. Nat Med, 2022, 28(7):1447-1454.
[12] Jarou ZJ, Dakka A, McGuire D, et al. ChatGPT versus human performance on emergency medicine board preparation questions[J]. Ann Emerg Med, 2024, 83(1):87-88.
[13] Ten Berg H, van Bakel B, van de Wouw L, et al. ChatGPT and generating a differential diagnosis early in an emergency department presentation[J]. Ann Emerg Med, 2024, 83(1):83-86.
[14] Hu DQ, Zhang SY, Liu Q, et al. Large language models in sum-marizing radiology report impressions for lung cancer in Chinese:evaluation study[J]. J Med Internet Res, 2025, 27:e65547.
[15] Raita Y, Goto T, Faridi MK, et al. Emergency department triage prediction of clinical outcomes using machine learning mo-dels[J]. Crit Care, 2019, 23(1):64.
[16] Farrohknia N, Castrén M, Ehrenberg A, et al. Emergency de-partment triage scales and their components:a systematic re-view of the scientific evidence[J]. Scand J Trauma Resusc Emerg Med, 2011, 19:42.
[17] Porto BM. Improving triage performance in emergency depart-ments using machine learning and natural language process-ing:a systematic review[J]. BMC Emerg Med, 2024, 24(1):219.
[18] Williams CYK, Zack T, Miao BY, et al. Use of a large lan-guage model to assess clinical acuity of adults in the emer-gency department[J]. JAMA Netw Open, 2024, 7(5):e248895.
[19] Marsilio M, Roldan ET, Salmasi L, et al. Operations manage-ment solutions to improve ED patient flows:evidence from the Italian NHS[J]. BMC Health Serv Res, 2022, 22(1):974.
[20] Leung F, Lau YC, Law M, et al. Artificial intelligence and end user tools to develop a nurse duty roster scheduling system[J]. Int J Nurs Sci, 2022, 9(3):373-377.
[21] Komorowski M, Celi LA, Badawi O, et al. The Artificial Intel-ligence Clinician learns optimal treatment strategies for sepsis in intensive care[J]. Nat Med, 2018, 24(11):1716-1720.
[22] Peine A, Hallawa A, Bickenbach J, et al. Development and vali-dation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care[J]. NPJ Digit Med, 2021, 4(1):32.
[23] 袁翔, 蔡学良, 朱文锋, 等. 基于人工智能行为识别实现护理员异常护理行为的监测及预警:CN202211332010.6[P]. 2023-02-03.
  Yuan X, Cai XL, Zhu WF, et al. Monitoring and early warning of abnormal nursing behaviors of nursing staff based on artificial intelligence behavior recognition:CN202211332010.6[P]. 2023-02-03.
[24] 王星尧, 李茜, 马彩云, 等. 穿戴式心电监测中的AI计算[J]. 生物医学工程学杂志, 2023, 40(6):1084-1092,1101.
  Wang XY, Li Q, Ma CY, et al. Artificial intelligence in wea-rable electrocardiogram monitoring[J]. J Biomed Eng, 2023, 40(6):1084-1092,1101.
[25] Chen MC, Huang TY, Chen TY, et al. Clinical narrative-aware deep neural network for emergency department critical out-come prediction[J]. J Biomed Inform, 2023, 138:104284.
[26] Yoon JH, Jeanselme V, Dubrawski A, et al. Prediction of hypo-tension events with physiologic vital sign signatures in the intensive care unit[J]. Crit Care, 2020, 24(1):661.
[27] Duffy WJ, Kharasch MS, Du HY. Point of care documentation impact on the nurse-patient interaction[J]. Nurs Adm Q, 2010, 34(1):E1-E10.
[28] Gesner E, Gazarian P, Dykes P. The burden and burnout in documenting patient care:an integrative literature review[J]. Stud Health Technol Inform, 2019, 264:1194-1198.
[29] Urquhart E, Ryan J, Hartigan S, et al. A pilot feasibility study comparing large language models in extracting key informa-tion from ICU patient text records from an Irish population[J]. Intensive Care Med Exp, 2024, 12(1):71.
[30] de Cezar AG, Del Castanhel F, Grosseman S. Needs of family members of patients in intensive care and their perception of medical communication[J]. Crit Care Sci, 2023, 35(1):73-83.
[31] Scquizzato T, Semeraro F, Swindell P, et al. Testing ChatGPT abi-lity to answer laypeople questions about cardiac arrest and car-diopulmonary resuscitation[J]. Resuscitation, 2024, 194:110077.
[32] 冉林, 谷新, 姜方清, 等. 多模态医学影像融合与人工智能技术在宫颈癌AI辅助诊断中的应用效果观察[J]. 影像科学与光化学, 2025, 43(6):63-69.
  Ran L, Gu X, Jiang FQ, et al. Application of multi-modal medical image fusion and AI technology in assisted diagnosis of cervical cancer[J]. Imag Sci Photochem, 2025, 43(6):63-69.
[33] 宋宇, 马涛洪, 张利帆, 等. 麻醉护理门诊健康教育智能问答系统的构建[J]. 护理学杂志, 2025, 40(21):16-20.
  Song Y, Ma TH, Zhang LF, et al. Construction of an intel-ligent question answering system for health education in anesthesia nursing outpatient[J]. J Nurs Sci, 2025, 40(21):16-20.
[34] Liu JL, Wang CY, Liu SR. Utility of ChatGPT in clinical pra-ctice[J]. J Med Internet Res, 2023, 25:e48568.
[35] Kuroiwa T, Sarcon A, Ibara T, et al. The potential of ChatGPT as a self-diagnostic tool in common orthopedic diseases:ex-ploratory study[J]. J Med Internet Res, 2023, 25:e47621.
[36] Lu YQ, Wu HY, Qi SY, et al. Artificial intelligence in inten-sive care medicine:toward a ChatGPT/GPT-4 way?[J]. Ann Biomed Eng, 2023, 51(9):1898-1903.
[37] Da Silva M, Horsley T, Singh D, et al. Legal concerns in heal-th-related artificial intelligence:a scoping review protocol[J]. Syst Rev, 2022, 11(1):123.
[38] Lazar S, Nelson A. AI safety on whose terms?[J]. Science, 2023, 381(6654):138.
[39] Li Y, Wang M, Wang L, et al. Advances in the application of AI robots in critical care:scoping review[J]. J Med Internet Res, 2024, 26:e54095.
[40] Howard A, Hope W, Gerada A. ChatGPT and antimicrobial ad-vice:the en.d of the consulting infection doctor?[J]. Lancet Infect Dis, 2023, 23(4):405-406.
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