Beyond the chain of survival: a scoping review of artificial intelligence applications in cardiac arrest

doi:10.5847/wjem.j.1920-8642.2026.025

Abstract

Abstract:

BACKGROUND: To provide a comprehensive analysis of the landscape of artificial intelligence (AI) applications in cardiac arrest (CA).

METHODS: Comprehensive searches were conducted in PubMed, the Cochrane Library, Web of Science, and EMBASE from database inception through 10 June 2025. Studies that applied AI in both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) populations across the following domains were included: prediction of cardiac arrest occurrence, prognostication of CA outcomes, applications of large language models (LLMs), and evaluation of cardiopulmonary resuscitation (CPR) and other AI-driven interventions related to CA.

RESULTS: The scoping review included 114 studies, encompassing data from 9,574,462 patients in total. AI was most commonly applied to the prediction of CA (overall, n=40; IHCA, n=30; OHCA, n=4; and both, n=6), CPR-related decision support during CA (n=16), and post-arrest prognosis and rehabilitation outcomes (overall, n=38; OHCA, n=21; IHCA, n=3; and both, n=14). Additional application areas included LLM-based applications (n=8), emergency call handling (n=4), wearable device-based detection (n=3), heart rhythm identification (n=2), education (n=2), and extracorporeal cardiopulmonary resuscitation (ECPR) candidate identification (n=1). Across all application scenarios, the highest area under the receiver operating characteristic curve (AUROC) value for pre-arrest CA prediction in IHCA patients was 0.998 using a multilayer perceptron (MLP) model, whereas the optimal AUROC for pre-arrest CA prediction in OHCA patients was 0.950 using extreme gradient boosting (XGBoost) or random forest (RF) models. For CPR-related decision support during CA, the highest AUROC achieved was 0.990 with a convolutional neural network (CNN) model. In prognostic prediction, the optimal AUROC for IHCA patients was 0.960 using XGBoost, while for OHCA patients it reached 0.976 using an MLP model.

CONCLUSION: This review shows that AI is most commonly used for the prediction of CA and CPR-related support, as well as post-arrest and rehabilitation outcomes. Future research directions include drug discovery, post-resuscitation management, neurorehabilitation, and clinical trial innovation. Further studies should prioritize multicenter clinical trials to evaluate AI models in real-world settings and validate their effectiveness across diverse patient populations. Overall, AI has significant potential to improve clinical practice, and its role in CA application is increasingly important.

Key words: Cardiac arrest, Artificial intelligence, Machine learning, Large language model, Scoping review

Xing Luo, Jinzhao Zhang, Fanrong Lin, Siqi Liu, Zhengfei Yang. Beyond the chain of survival: a scoping review of artificial intelligence applications in cardiac arrest[J]. World Journal of Emergency Medicine, 2026, 17(1): 7-14.

Figures/Tables 4

Figure 1.

Table 1.

Figure 2.

Figure 3.

References 42

1	Greif R, Bray JE, Djärv T, Drennan IR, Liley HG, Ng KC, et al. 2024 international consensus on cardiopulmonary resuscitation and emergency cardiovascular care science with treatment recommendations: summary from the basic life support; advanced life support; pediatric life support; neonatal life support; education, implementation, and teams; and First Aid Task Forces. Circulation. 2024; 150(24):e580-e687.
2	Xiao LL, Chen XY, Wang FL, Qin YP, Zhou PF, Li F, et al. Research progress of systemic inflammatory response after cardiac arrest. Chine J Emerg Med. 2024; 33(7):1058-66.
3	Gräsner JT, Herlitz J, Tjelmeland IBM, Wnent J, Masterson S, Lilja G, et al. European Resuscitation Council guidelines 2021: epidemiology of cardiac arrest in Europe. Resuscitation. 2021;161: 61-79.
4	Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. Heart disease and stroke statistics-2023 update: a report from the American Heart Association. Circulation. 2023; 147(8): e93-e621.
5	Andersen LW, Holmberg MJ, Berg KM, Donnino MW, Granfeldt A. In-hospital cardiac arrest: a review. JAMA. 2019; 321(12): 1200-10. doi: 10.1001/jama.2019.1696 pmid: 30912843
6	Li SW, Qin CZ, Zhang HJ, Maimaitiming M, Shi JY, Feng YK, et al. Survival after out-of-hospital cardiac arrest before and after legislation for bystander CPR. JAMA Netw Open. 2024; 7(4): e247909. doi: 10.1001/jamanetworkopen.2024.7909
7	Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019; 25(1): 44-56. doi: 10.1038/s41591-018-0300-7 pmid: 30617339
8	Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018; 2(10): 719-31. doi: 10.1038/s41551-018-0305-z
9	Chang CY, Chen CC, Tsai ML, Hsieh MJ, Chen TH, Chen SW, et al. Predicting mortality and hospitalization in heart failure with preserved ejection fraction by using machine learning. JACC Asia. 2024; 4(12):956-68. doi: 10.1016/j.jacasi.2024.09.003
10	Huerta N, Rao SJ, Isath A, Wang Z, Glicksberg BS, Krittanawong C. The premise, promise, and perils of artificial intelligence in critical care cardiology. Prog Cardiovasc Dis. 2024;86: 2-12.
11	Du C, Zhou J, Yu YX. Research trends in the application of artificial intelligence in nursing of chronic disease: a bibliometric and network visualization study. Front Digit Health. 2025;7: 1608266.
12	Jiang S, Liu X, Liu T, Gu Y, An B, Wang C, et al. Performance of a novel medical artificial intelligence large language model on supporting decision-making for emergency patients with suspected sepsis. World J Emerg Med. 2025; 16(5):447-55. doi: 10.5847/wjem.j.1920-8642.2025.095 pmid: 40979784
13	Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg. 2021;88: 105906.
14	Alamgir A, Mousa O, Shah Z. Artificial intelligence in predicting cardiac arrest: scoping review. JMIR Med Inform. 2021; 9(12): e30798. doi: 10.2196/30798
15	Toy J, Bosson N, Schlesinger S, Gausche-Hill M, Stratton S. Artificial intelligence to support out-of-hospital cardiac arrest care: a scoping review. Resusc Plus. 2023;16: 100491.
16	Viderman D, Abdildin YG, Batkuldinova K, Badenes R, Bilotta F. Artificial intelligence in resuscitation: a scoping review. J Clin Med. 2023; 12(6): 2254. doi: 10.3390/jcm12062254
17	Boyce LW, Goossens PH. Rehabilitation after cardiac arrest: integration of neurologic and cardiac rehabilitation. Semin Neurol. 2017; 37(1): 94-102. doi: 10.1055/s-0036-1593860 pmid: 28147424
18	Afridi A, Obaid S, Raheel N, Rathore FA. Integrating artificial intelligence in stroke rehabilitation: current trends and future directions; a mini review. J Pak Med Assoc. 2025; 75(2): 445-7. doi: 10.47391/JPMA.25-16 pmid: 39948807
19	Zhang K, Yang X, Wang YF, Yu YF, Huang N, Li G, et al. Artificial intelligence in drug development. Nat Med. 2025; 31(1): 45-59. doi: 10.1038/s41591-024-03434-4 pmid: 39833407
20	Jarallah SJ, Almughem FA, Alhumaid NK, Al Fayez N, Alradwan I, Alsulami KA, et al. Artificial intelligence revolution in drug discovery: a paradigm shift in pharmaceutical innovation. Int J Pharm. 2025;680: 125789.
21	Sarvepalli S, Vadarevu S. Role of artificial intelligence in cancer drug discovery and development. Cancer Lett. 2025;627: 217821.
22	Rhee TM, Ko YK, Kim HK, Lee SB, Kim BS, Choi HM, et al. Machine learning-based discrimination of cardiovascular outcomes in patients with hypertrophic cardiomyopathy. JACC Asia. 2024; 4(5):375-86. doi: 10.1016/j.jacasi.2023.12.001
23	Liu Q, Huang RH, Hsieh J, Zhu H, Tiwari M, Liu GS, et al. Landscape analysis of the application of artificial intelligence and machine learning in regulatory submissions for drug development from 2016 to 2021. Clin Pharmacol Ther. 2023; 113(4): 771-4. doi: 10.1002/cpt.v113.4
24	Song W. Current and emerging innovations in technology, strategy, and artificial intelligence: a new era in cardiac arrest and cardiopulmonary resuscitation. World J Emerg Med. 2025; 16(6):609-13. doi: 10.5847/wjem.j.1920-8642.2025.120
25	Xu ZJ, Ren F, Wang P, Cao J, Tan CT, Ma DD, et al. A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial. Nat Med. 2025; 31(8): 2602-10. doi: 10.1038/s41591-025-03743-2
26	Jong M. Transforming medical education in the AI era: Balancing technological expertise with humanistic care in tomorrow’s doctors. Ann Acad Med Singap. 2024; 53(12): 754-7. doi: 10.47102/annals-acadmedsg.
27	Pinsky MR, Bedoya A, Bihorac A, Celi L, Churpek M, Economou-Zavlanos NJ, et al. Use of artificial intelligence in critical care: opportunities and obstacles. Crit Care. 2024; 28(1): 113. doi: 10.1186/s13054-024-04860-z
28	Niccol T, Young M, Holmes NE, Kishore K, Amjad S, Gaca M, et al. Hallucinations and disturbed behaviour in the critically ill: incidence, patient characteristics, associations, trajectory, and outcomes. Crit Care. 2025; 29(1): 54. doi: 10.1186/s13054-025-05290-1
29	Maddox TM, Embí P, Gerhart J, Goldsack J, Parikh RB, Sarich TC. Generative AI in medicine - evaluating progress and challenges. N Engl J Med. 2025; 392(24): 2479-83. doi: 10.1056/NEJMsb2503956
30	Wu XT, Huang Y, He Q. A large language model improves clinicians’ diagnostic performance in complex critical illness cases. Crit Care. 2025; 29(1): 230. doi: 10.1186/s13054-025-05468-7
31	Iqbal U, Tanweer A, Rahmanti AR, Greenfield D, Lee LT, Li YJ. Impact of large language model (ChatGPT) in healthcare: an umbrella review and evidence synthesis. J Biomed Sci. 2025; 32(1): 45. doi: 10.1186/s12929-025-01131-z pmid: 40335969
32	Yang J, Dung NT, Thach PN, Phong NT, Phu VD, Phu KD, et al. Generalizability assessment of AI models across hospitals in a low-middle and high income country. Nat Commun. 2024; 15(1): 8270. doi: 10.1038/s41467-024-52618-6 pmid: 39333515
33	Miglietta L, Rawson TM, Galiwango R, Tasker A, Ming DK, Akogo D, et al. Artificial intelligence and infectious disease diagnostics: state of the art and future perspectives. Lancet Infect Dis. 2025: S1473-S3099(25)00354-8.
34	Molière S, Hamzaoui D, Ploussard G, Mathieu R, Fiard G, Baboudjian M, et al. A systematic review of the diagnostic accuracy of deep learning models for the automatic detection, localization, and characterization of clinically significant prostate cancer on magnetic resonance imaging. Eur Urol Oncol. 2025; 8(4): 1182-202.
35	Chatzichristos C, Chatzichristos G, Borremans I, Gruyaert S, de Vos I, de Vos M, et al. Bridging the AI-literacy gap in health care: qualitative analysis of the Flanders case study. J Med Internet Res. 2025;27: e76709.
36	Perry J, Brody JA, Fong C, Sunshine JE, O’Reilly-Shah VN, Sayre MR, et al. Predicting out-of-hospital cardiac arrest in the general population using electronic health records. Circulation. 2024; 150(2): 102-10. doi: 10.1161/CIRCULATIONAHA.124.069105 pmid: 38860364
37	Andersson A, Arctaedius I, Cronberg T, Levin H, Nielsen N, Friberg H, et al. In-hospital versus out-of-hospital cardiac arrest: characteristics and outcomes in patients admitted to intensive care after return of spontaneous circulation. Resuscitation. 2022;176: 1-8.
38	Motazedian P, Marbach JA, Prosperi-Porta G, Parlow S, Di Santo P, Abdel-Razek O, et al. Diagnostic accuracy of point-of-care ultrasound with artificial intelligence-assisted assessment of left ventricular ejection fraction. NPJ Digit Med. 2023; 6(1): 201. doi: 10.1038/s41746-023-00945-1 pmid: 37898711
39	Chikatimalla R, Trivedi YV, Chigurupati HD, Bin Faheem MS, Naveed MA, Sattar Y, et al. From prediction to precision: artificial intelligence and emerging technologies in cardiopulmonary resuscitation. Prog Cardiovasc Dis. 2025;92: 97-107.
40	Nettinger M, Wittig J, Riis DN, Løfgren B, Lauridsen KG. Associations of manual defibrillator compared to automated external defibrillator usage with defibrillation and resuscitation quality during in-hospital cardiac arrest. Resuscitation. 2025;212: 110619.
41	Baeßler B, Engelhardt S, Hekalo A, Hennemuth A, Hüllebrand M, Laube A, et al. Perfect match: radiomics and artificial intelligence in cardiac imaging. Circ Cardiovasc Imaging. 2024; 17(6): e015490.
42	Ahn S, Jung S, Park JH, Cho H, Moon S, Lee S. Artificial intelligence for predicting shockable rhythm during cardiopulmonary resuscitation: in-hospital setting. Resuscitation. 2024;202: 110325.

Stage of care	Setting	Key AI applications	Number of studies	Representative algorithms/models*	Best reported performance (AUROC)
Pre-arrest	IHCA	Early cardiac arrest prediction	30	RF; LR; SVM¹	0.998 (MLP)
Pre-arrest	OHCA	Early cardiac arrest prediction	4	RF; SVM; XGBoost²	0.950 (XGBoost & RF)
Pre-arrest	IHCA & OHCA	Early cardiac arrest prediction	6	SVM; ANN³	0.892 (SVM)
Intra-arrest	IHCA & OHCA	Cardiopulmonary resuscitation support	16	CNN; SVM⁴	0.990 (CNN)
Post-arrest	IHCA	Prognosis and rehabilitation	3	LR; XGBoost	0.960 (XGBoost)
Post-arrest	OHCA	Prognosis and rehabilitation	21	RF; LR; XGBoost⁵	0.976 (MLP)
Post-arrest	IHCA & OHCA	Prognosis and rehabilitation	14	RF; LR; XGBoost⁶	0.965 (XGBoost)
Others	OHCA	Emergency call of cardiac arrest recognition	4	RF; ANN	0.749 (RF)
Others	IHCA & OHCA	Large language model applications	8	GPT-4; GPT-3.5	0.850 (GPT-4)
Others	IHCA & OHCA	Wearable-based cardiac arrest detection	3	RF; ANN	Not applicable in this study
Others	IHCA & OHCA	Cardiac rhythm identification	2	SVM; RF; ANN; U-Net	Not applicable in this study
Others	IHCA & OHCA	ECPR identification	1	RF	0.810 (RF)
Others	IHCA & OHCA	AI-assisted cardiac arrest education	2	RF	Not applicable in this study

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