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中华危重症医学杂志(电子版) ›› 2025, Vol. 18 ›› Issue (02) : 162 -168. doi: 10.3877/cma.j.issn.1674-6880.2025.02.014

综述

人工智能在重症医学中的应用现状及展望
王壮1, 倪海滨2,()   
  1. 1. 210028 南京,江苏省中西医结合医院(南京中医药大学附属中西医结合医院)急诊医学科
    2. 210028 南京,江苏省中西医结合医院(南京中医药大学附属中西医结合医院)重症医学科
  • 收稿日期:2024-06-19 出版日期:2025-04-30
  • 通信作者: 倪海滨

Zhuang Wang, Haibin Ni()   

  • Received:2024-06-19 Published:2025-04-30
  • Corresponding author: Haibin Ni
引用本文:

王壮, 倪海滨. 人工智能在重症医学中的应用现状及展望[J/OL]. 中华危重症医学杂志(电子版), 2025, 18(02): 162-168.

Zhuang Wang, Haibin Ni. [J/OL]. Chinese Journal of Critical Care Medicine(Electronic Edition), 2025, 18(02): 162-168.

1
Mamdani M, Slutsky AS. Artificial intelligence in intensive care medicine[J]. Intensive Care Med, 2021,47 (2): 147-149.
2
Doya K, Friston K, Sugiyama M, et al. Neural networks special issue on artificial intelligence and brain science[J]. Neural Netw, 2022, 155: 328-329.
3
Komorowski M. Artificial intelligence in intensive care: are we there yet?[J]. Intensive Care Med, 2019,45 (9): 1298-1300.
4
Ooi SKG, Makmur A, Soon AYQ, et al. Attitudes toward artificial intelligence in radiology with learner needsassessmentwithinradiologyresidency programmes: a national multi-programme survey [J].Singapore Med J, 2021, 62 (3): 126-134.
5
Pinto Dos Santos D, Giese D, Brodehl S, et al.Medical students' attitude towards artificial intelligence:a multicentre survey [J]. Eur Radiol, 2019, 29 (4):1640-1646.
6
Fleuren LM, Klausch TLT, Zwager CL, et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy[J]. Intensive Care Med, 2020, 46 (3): 383-400.
7
Li L, Qin L, Xu Z, et al. Using artificial intelligence todetectCOVID-19andcommunity-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy[J]. Radiology, 2020, 296 (2): E65-E71.
8
Dreizin D, Zhou Y, Fu S, et al. A multiscale deep learning method for quantitative visualization of traumatic hemoperitoneum at CT: assessment of feasibility and comparison with subjective categorical estimation [J].Radiol Artif Intell, 2020, 2 (6):e190220.
9
Emakhu J, Monplaisir L, Aguwa C, et al. Acute coronary syndrome prediction in emergency care: a machine learning approach [J].Comput Methods Programs Biomed, 2022, 225: 107080.
10
Yadgir SR, Engstrom C, Jacobsohn GC, et al.Machine learning-assisted screening for cognitive impairment in the emergency department [J]. J Am Geriatr Soc, 2022, 70 (3): 831-837.
11
孙保学. 人工智能辅助医疗决策并未挑战尊重自主原则[J]. 伦理学研究,2019(6):81-86.
12
Anesi GL, Admon AJ, Halpern SD, et al.Understanding irresponsible use of intensive care unit resources in the USA[J]. Lancet Respir Med, 2019, 7(7): 605-612.
13
Castela Forte J, Perner A, van der Horst ICC. The use of clustering algorithms in critical care research to unravel patient heterogeneity [J]. Intensive Care Med,2019, 45 (7): 1025-1028.
14
Kent DM, Steyerberg E, van Klaveren D. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects [J]. BMJ, 2018, 363:k4245.
15
Hinton G.Deep learning-a technology with the potential to transform health care [J]. JAMA, 2018,320 (11): 1101-1102.
16
McWilliams CJ, Lawson DJ, Santos-Rodriguez R, et al.Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK[J]. BMJ Open, 2019, 9 (3): e25925.
17
伊孙邦,胡雨峰,林素涵,等. 不同时间段血乳酸水平对脓毒症院内死亡的预测价值比较:基于重症监护医学信息数据库[J/OL]. 中华危重症医学杂志(电子版),2020,13(1):39-43.
18
Sotoodeh M, Ho JC.Improving length of stay prediction using a hidden Markov model [J]. AMIA Jt Summits Transl Sci Proc, 2019, 2019: 425-434.
19
Nanayakkara S, Fogarty S, Tremeer M, et al.Characterising risk of in-hospital mortality following cardiac arrest using machine learning: a retrospective international registry study [J]. PLoS Med, 2018, 15(11): e1002709.
20
Nemati S, Holder A, Razmi F, et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU [J]. Crit Care Med, 2018, 46 (4):547-553.
21
Marafino BJ, Park M, Davies JM, et al. Validation of prediction models for critical care outcomes using natural language processing of electronic health record data[J]. JAMA Netw Open, 2018, 1 (8): e185097.
22
Jones D, Mitchell I, Hillman K, et al. Defining clinical deterioration [J]. Resuscitation, 2013, 84 (8):1029-1034.
23
Escobar GJ, Liu VX, Schuler A, et al. Automated identification of adults at risk for in-hospital clinical deterioration[J]. N Engl J Med, 2020, 383 (20): 1951-1960.
24
Romero-Brufau S, Whitford D, Johnson MG, et al.Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic early warning score (MC-EWS)[J]. J Am Med Inform Assoc,2021, 28 (6): 1207-1215.
25
Pimentel MAF, Redfern OC, Malycha J, et al.Detecting deteriorating patients in the hospital:development and validation of a novel scoring system[J]. Am J Respir Crit Care Med, 2021, 204 (1): 44-52.
26
Rhee C, Jones TM, Hamad Y, et al. Prevalence,underlying causes, and preventability of sepsisassociated mortality in US Acute Care Hospitals[J].JAMA Netw Open, 2019, 2 (2): e187571.
27
袁琪茜,陈宇,杨晓玲,等. 人工智能决策系统在脓毒症管理中的应用前景[J/OL]. 中华危重症医学杂志(电子版),2021,14(5):416-419.
28
Beganovic M, McCreary EK, Mahoney MV, et al.Interplaybetween rapid diagnostictests and antimicrobial stewardship programs among patients with bloodstream and other severe infections[J]. J Appl Lab Med, 2019, 3 (4): 601-616.
29
Viasus D, Puerta-Alcalde P, Cardozo C, et al.Predictorsofmultidrug-resistantPseudomonas aeruginosa in neutropenic patients with bloodstream infection[J]. Clin Microbiol Infect, 2020, 26 (3): 345-350.
30
Garcia-Vidal C, Cardozo-Espinola C, Puerta-Alcalde P,et al. Risk factors for mortality in patients with acute leukemia and bloodstream infections in the era of multiresistance[J]. PLoS One, 2018, 13 (6): e199531.
31
Goodman KE, Lessler J, Cosgrove SE, et al. A clinical decision tree to predict whether a bacteremic patient is infected with an extended-spectrum β-Lactamase-producing organism [J]. Clin Infect Dis,2016, 63 (7): 896-903.
32
MacFadden DR, Coburn B, Shah N, et al. Decisionsupport models for empiric antibiotic selection in Gram-negative bloodstream infections[J]. Clin Microbiol Infect, 2019, 25 (1): 108.e1-108.e7.
33
Shimabukuro DW, Barton CW, Feldman MD, et al.Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial [J]. BMJ Open Respir Res, 2017, 4 (1): e000234.
34
Lamping F, Jack T, Rubsamen N, et al. Development and validation of a diagnostic model for early differentiation of sepsis and non-infectious SIRS in critically ill children - a data-driven approach using machine-learning algorithms[J]. BMC Pediatr, 2018, 18(1): 112.
35
Vasala A, Hytonen VP, Laitinen OH. Modern tools for rapid diagnostics of antimicrobial resistance [J].Front Cell Infect Microbiol, 2020, 10: 308.
36
Eickelberg G, Sanchez-Pinto LN, Luo Y. Predictive modeling of bacterial infections and antibiotic therapy needs in critically ill adults [J]. J Biomed Inform,2020, 109: 103540.
37
王静,周云英. 急性心肌梗死急诊PCI 术后护理风险预警模型的构建与应用[J]. 介入放射学杂志,2021,30(2):196-200.
38
Yoon JH, Mu L, Chen L, et al.Predicting tachycardia as a surrogate for instability in the intensive care unit[J]. J Clin Monit Comput, 2019, 33(6): 973-985.
39
Giannini HM, Ginestra JC, Chivers C, et al. A machine learning algorithm to predict severe sepsis and septic shock: development, implementation, and impact on clinical practice [J]. Crit Care Med, 2019,47 (11): 1485-1492.
40
Banerjee I, Sofela M, Yang J, et al. Development and performance of the pulmonary embolism result forecast model (PERFORM) for computed tomography clinical decision support [J]. JAMA Netw Open, 2019, 2 (8):e198719.
41
Zeiberg D, Prahlad T, Nallamothu BK, et al. Machine learning for patient risk stratification for acute respiratory distress syndrome [J]. PLoS One, 2019, 14(3): e214465.
42
Wang R, Dai W, Gong J, et al. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients[J]. J Hematol Oncol, 2022, 15 (1):11.
43
Komorowski M, Celi LA, Badawi O, et al. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care [J]. Nat Med,2018, 24 (11): 1716-1720.
44
Peine A, Hallawa A, Bickenbach J, et al.Development and validation of a reinforcement learning algorithmtodynamicallyoptimizemechanical ventilation in critical care[J]. NPJ Digit Med, 2021, 4(1): 32.
45
Davoudi A, Malhotra KR, Shickel B, et al. Intelligent ICU for autonomous patient monitoring using pervasive sensing and deep learning [J]. Sci Rep, 2019, 9 (1):8020.
46
Smith H.Clinical AI: opacity, accountability,responsibility and liability [J]. AI Soc, 2021, 36 (2):535-545.
47
Floridi L, Cowls J. A unified framework of five principles for AI in society [M]. Switzerland: Springer International Publishing AG, 2021: 5-17.
48
Morley J, Machado CCV, Burr C, et al. The ethics of AI in health care: a mapping review[J]. Soc Sci Med,2020, 260: 113172.
49
Levin S, Toerper M, Hamrock E, et al. Machinelearning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index [J]. Ann Emerg Med, 2018, 71 (5): 565-574.e2.
50
Chen M, Decary M.Artificial intelligence in healthcare: an essential guide for health leaders [J].Healthc Manage Forum, 2020, 33 (1): 10-18.
51
Price WN, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence[J]. JAMA, 2019,322 (18): 1765-1766.
52
Jobson D, Mar V, Freckelton I. Legal and ethical considerations of artificial intelligence in skin cancer diagnosis[J]. Australas J Dermatol, 2022, 63 (1): e1-e5.
53
Duffourc MN, Gerke S. The proposed EU directives for AI liability leave worrying gaps likely to impact medical AI[J]. NPJ digital medicine, 2023, 6 (1): 77.
54
Mezrich JL. Demystifying medico-legal challenges of artificial intelligence applications in molecular imaging and therapy[J]. PET Clinics, 2022, 17 (1): 41-49.
55
Bahl M. Artificial intelligence in clinical practice:implementation considerations and barriers[J]. J Breast Imaging, 2022, 4 (6): 632-639.
56
Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery [J]. Lancet Oncol,2019, 20 (5): e262-e273.
57
俞丽,陶敏敏,谢凌丽,等. 人工智能医学影像与数字病理学在胃癌诊疗及预后评估中的应用进展[J]. 中华消化外科杂志,2025,24(1):137-142.
58
Sjoding MW, Hofer TP, Co I, et al. Interobserver reliability of the Berlin ARDS definition and strategies to improve the reliability of ARDS diagnosis[J]. Chest,2018, 153 (2): 361-367.
59
Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead[J]. Nat Mach Intell, 2019, 1 (5): 206-215.
60
Durán JM, Jongsma KR. Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI [J].J Med Ethics, 2021:medethics-2020-106820.
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