Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study
Open accessAbstract
BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing. METHODS: In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems. FINDINGS: 45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 [0·77-0·92]), non-cardiac surgery (AUC 0·83 [0·79-0·88]), and catheterisation or endoscopy suite procedures (AUC 0·76 [0·72-0·81]). INTERPRETATION: A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications. FUNDING: National Heart, Lung, and Blood Institute.
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AI performance frequently approaches but does not surpass specialist-level interpretation.
“This aligns with broader meta-analytic observations that AI performance frequently approaches but does not surpass specialist-level interpretation5,10-12.”
From Structural error asymmetry and harm-weighted analysis of ChatGPT versus ICU Physicians in acid–base interpretation: a prospective observational study (2026) · cites this paper as [10]
The Results compare the ECG algorithm with a clinical risk score, and the Discussion says it outperformed human-interpretable ECG features. Neither section reports a meta-analysis or compares AI performance with specialist-level interpretation. Searches for specialist and expert interpretation found no passage establishing the claimed pattern. (LLM, conf=0.76)
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Lee, T. H.-Y., Marcantonio, E. R., Mangione, C. M., Thomas, E. J., Polanczyk, C. A., Cook, E. F., Sugarbaker, D. J., Donaldson, M. C., Poss, R., Ho, K. K. L., et al. (1999) · Circulation · doi:10.1161/01.cir.100.10.1043 · Cited by 3,749 · see citing works
International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287 · see citing works
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0195-0 · Cited by 1,865 · see citing works
Bilimoria, K., Liu, Y., Paruch, J., Zhou, L., Kmiecik, T. E., Ko, C. Y., & Cohen, M. E. (2013) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2013.07.385 · Cited by 1,854 · see citing works
Fleisher, L. A., Fleischmann, K. E., Auerbach, A. D., Barnason, S. A., Beckman, J. A., Bozkurt, B., Dávila‐Román, V. G., Gerhard‐Herman, M. D., Holly, T. A., Kane, G. C., et al. (2014) · Circulation · doi:10.1161/cir.0000000000000106 · Cited by 1,828 · see citing works
Attia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., Pellikka, P. A., Enriquez-Sarano, M., Noseworthy, P. A., Munger, T. M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0240-2 · Cited by 1,389 · see citing works
Fleisher, L. A., Fleischmann, K. E., Auerbach, A. D., Barnason, S. A., Beckman, J. A., Bozkurt, B., Dávila‐Román, V. G., Gerhard‐Herman, M. D., Holly, T. A., Kane, G. C., et al. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2014.07.944 · Cited by 1,323 · see citing works
Video-based AI for beat-to-beat assessment of cardiac function
Ouyang, D., He, B., Ghorbani, A., Yuan, N., Ebinger, J., Langlotz, C. P., Heidenreich, P. A., Harrington, R. A., Liang, D. H., Ashley, E. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2145-8 · Cited by 1,153 · see citing works
Self-citationDevereaux, P. J., Chan, M. T. V., Alonso‐Coello, P., Walsh, M., Berwanger, O. B., Villar, J. C., Wang, C. Y., Garutti, R. I., Jacka, M. J., Sigamani, A., et al. (2012) · JAMA · doi:10.1001/jama.2012.5502 · Cited by 1,102 · see citing works
Artificial intelligence-enhanced electrocardiography in cardiovascular disease management
Siontis, K. C., Noseworthy, P. A., Attia, Z. I., & Friedman, P. A. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00503-2 · Cited by 914 · see citing works
Perioperative Beta-Blocker Therapy and Mortality after Major Noncardiac Surgery
Lindenauer, P. K., Pekow, P. S., Wang, K., Mamidi, D. K., Gutiérrez, B., & Benjamin, E. M. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa041895 · Cited by 855 · see citing works
Practice Advisory for Preanesthesia Evaluation
Apfelbaum, J. L., Connis, R. T., Nickinovich, D. G., Pasternak, L. R., Arens, J. F., Caplan, R. A., Connis, R. T., Fleisher, L. A., Flowerdew, R. M., Gold, B. S., et al. (2012) · Anesthesiology · doi:10.1097/aln.0b013e31823c1067 · Cited by 845 · see citing works
Development and Validation of a Risk Calculator for Prediction of Cardiac Risk After Surgery
Gupta, P. K., Gupta, H., Sundaram, A., Kaushik, M., Fang, X., Miller, W. J., Esterbrooks, D. J., Hunter, C. B., Pipinos, I. I., Johanning, J. M., et al. (2011) · Circulation · doi:10.1161/circulationaha.110.015701 · Cited by 753 · see citing works
Ford, M. K., Beattie, W. S., & Wijeysundera, D. N. (2010) · Annals of Internal Medicine · doi:10.7326/0003-4819-152-1-201001050-00007 · Cited by 549 · see citing works
Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs
Attia, Z. I., Friedman, P. A., Noseworthy, P. A., López-Jiménez, F. A., Ladewig, D. J., Satam, G., Pellikka, P. A., Munger, T. M., Asirvatham, S. J., Scott, C. G., et al. (2019) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.119.007284 · Cited by 459 · see citing works
Suchard, M. A., Schuemie, M. J., Krumholz, H. M., You, S. C., Chen, R., Pratt, N., Reich, C. G., Duke, J., Madigan, D., Hripcsak, G., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)32317-7 · Cited by 359 · see citing works
The Surgical Mortality Probability Model
Glance, L. G., Lustik, S. J., Hannan, E. L., Osler, T. M., Mukamel, D. B., Qian, F., & Dick, A. W. (2012) · Annals of Surgery · doi:10.1097/sla.0b013e31824b45af · Cited by 330 · see citing works
Galloway, C. D., Valys, A. V., Shreibati, J. B., Treiman, D. L., Petterson, F. L., Gundotra, V. P., Albert, D. E., Attia, Z. I., Carter, R. E., Asirvatham, S. J., et al. (2019) · JAMA Cardiology · doi:10.1001/jamacardio.2019.0640 · Cited by 318 · see citing works
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
Raghunath, S., Ulloa Cerna, A. E., Jing, L., vanMaanen, D. P., Stough, J., Hartzel, D. N., Leader, J. B., Kirchner, H. L., Stumpe, M. C., Hafez, A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0870-z · Cited by 314 · see citing works
Choi, J.-H., Cho, D. K., Song, Y.-B., Hahn, J., Choi, S., Gwon, H., Kim, D., Lee, S. H., Oh, J. K., & Jeon, E.-S. (2009) · Heart · doi:10.1136/hrt.2009.181388 · Cited by 154 · see citing works
Sheth, T., Chan, M. T. V., Butler, C., Chow, B. J. W., Tandon, V., Nägele, P., Mitha, A. S., Mrkobrada, M., Szczeklik, W., Faridah, Y., et al. (2015) · BMJ · doi:10.1136/bmj.h1907 · Cited by 146 · see citing works
Davis, C., Tait, G., Carroll, J., Wijeysundera, D. N., & Beattie, W. S. (2013) · Canadian Journal of Anesthesia/Journal canadien d anesthésie · doi:10.1007/s12630-013-9988-5 · Cited by 119 · see citing works
A deep learning algorithm to detect anaemia with ECGs: a retrospective, multicentre study
Kwon, J., Cho, Y., Jeon, K., Cho, S., Kim, K., Baek, S. D., Jeung, S., Park, J., & Oh, B. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30108-4 · Cited by 116 · see citing works
Fronczek, J., Polok, K., Devereaux, P. J., Górka, J., Archbold, A., Biccard, B. M., Duceppe, E., Manach, Y. L., Sessler, D. I., Duchińska, M., et al. (2019) · British Journal of Anaesthesia · doi:10.1016/j.bja.2019.05.029 · Cited by 62 · see citing works
Park, S., Choi, J., Cho, S., Chang, S., Choi, J.-O., Lee, S., Park, S. W., Oh, J. K., Kim, D., & Jeon, E. (2011) · Korean Circulation Journal · doi:10.4070/kcj.2011.41.9.505 · Cited by 58 · see citing works
Glance, L. G., Faden, E., Dutton, R. P., Lustik, S. J., Li, Y., Eaton, M. P., & Dick, A. W. (2018) · Anesthesiology · doi:10.1097/aln.0000000000002341 · Cited by 58 · see citing works
Rao, J. Y., Yeriswamy, M. C., Santhosh, M. J., Shetty, G. G., Varghese, K., Patil, C., & Iyengar, S. S. (2012) · Indian Heart Journal · doi:10.1016/s0019-4832(12)60047-9 · Cited by 31 · see citing works
Marston, N., Brenes, J., Garcia, S. A., Kuskowski, M. A., Adabağ, S., Santilli, S. M., & McFalls, E. O. (2011) · Journal of Critical Care · doi:10.1016/j.jcrc.2011.06.004 · Cited by 31 · see citing works
Singh, S., Fouayzi, H., Anzuoni, K., Goldman, L., Min, J. Y., Griffin, M. R., Grijalva, C. G., Morrow, J. A., Whitmore, C. C., Leonard, C. E., et al. (2018) · Drug Safety · doi:10.1007/s40264-018-0754-z · Cited by 18 · see citing works
Machado, M. D. N., Rodrigues, F. B., Nakazone, M. A., Martin, D. F., Sabbag, A. T. R., Grigolo, I. H., Silva, O. L., Maia, L. N., & Jaffe, A. S. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.120.018008 · Cited by 9 · see citing works
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Varghese, C., Harrison, E. M., O’Grady, G., & Topol, E. J. (2024) · Nature Medicine · doi:10.1038/s41591-024-02970-3 · Cited by 287 · see citing works
Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice
Khera, R., Oikonomou, E. K., Nadkarni, G. N., Morley, J., Wiens, J., Butte, A. J., & Topol, E. J. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.05.003 · Cited by 309 · see citing works
A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease
Hughes, J. W., Tooley, J., Soto, J. T., Ostropolets, A., Poterucha, T. J., Christensen, M. K., Yuan, N., Ehlert, B., Kaur, D., Kang, G., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00916-6 · Cited by 76 · see citing works
Self-citationAn ECG-based artificial intelligence model for assessment of sudden cardiac death risk
Holmström, L. T. A., Chugh, H. S., Nakamura, K., Bhanji, Z., Seifer, M., Uy‐Evanado, A., Reinier, K., Ouyang, D., & Chugh, S. S. (2024) · Communications Medicine · doi:10.1038/s43856-024-00451-9 · Cited by 68 · see citing works
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Chiu, I.-M., Wu, P.-J., Zhang, H., Hughes, J. W., Rogers, A. J., Jalilian, L., Perez, M., Lin, C.-H. R., Lee, C.-T., Zou, J., et al. (2024) · JACC: Clinical Electrophysiology · doi:10.1016/j.jacep.2024.07.023 · Cited by 31 · see citing works
Self-citationVrudhula, A., Stern, L. K., Cheng, P. P., Ricchiuto, P., Daluwatte, C., Witteles, R. M., Patel, J. K., & Ouyang, D. (2024) · JACC Advances · doi:10.1016/j.jacadv.2024.100998 · Cited by 27 · see citing works
Self-citationArtificial intelligence in cardiac surgery: A systematic review
Sulague, R. M., Beloy, F. J., Medina, J. R., Mortalla, E. D., Cartojano, T. D., Macapagal, S. C., & Kpodonu, J. (2024) · World Journal of Surgery · doi:10.1002/wjs.12265 · Cited by 24 · see citing works
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Ahn, K., Cho, M., Kim, S., Lee, K. E., Song, Y., Yoo, S., Jeon, S. Y., Kim, J. L., Yoon, D. H., & Kong, H. (2023) · Bioengineering · doi:10.3390/bioengineering10091093 · Cited by 21 · see citing works
Harris, C., Pimpalkar, A. S., Aggarwal, A., Yang, J., Chen, X., Schmidgall, S., Rapuri, S., Greenstein, J. L., Taylor, C. O., & Stevens, R. D. (2025) · British Journal of Anaesthesia · doi:10.1016/j.bja.2025.07.085 · Cited by 16 · see citing works
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Bedford, J., Redfern, O., O’Brien, B., & Watkinson, P. (2024) · Current Opinion in Anaesthesiology · doi:10.1097/aco.0000000000001445 · Cited by 18 · see citing works
Wang, Y., Ni, H., Zhou, J., Liu, L., Lin, J., Yin, M., Gao, J., Zhu, S., Yin, Q., Zhu, J., et al. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01123-9 · Cited by 12 · see citing works
Liu, T., Liang, L., Che, C., Liu, Y., & Jin, B. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104826 · Cited by 10 · see citing works
Yoon, H., Ahn, J. H., Kim, B. Y., Lee, H., Jo, W.-Y., Yoon, S., Park, H., & Lee, H. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000003143 · Cited by 8 · see citing works
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Vrudhula, A., Hughes, J. W., Yuan, N., & Ouyang, D. (2024) · NEJM AI · doi:10.1056/aics2300176 · Cited by 7 · see citing works
Self-citationXu, P., Liu, M., Liu, M., & Shen, A. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.09.001 · Cited by 8 · see citing works
Sahashi, Y., Vukadinovic, M., Duffy, G., Li, D., Cheng, S. C., Berman, D. S., Ouyang, D., & Kwan, A. C. (2025) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2025.05.016 · Cited by 6 · see citing works
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Lee, H. S., Croon, P. M., Lee, M. S., Poterucha, T. J., Lin, C. H., Son, J. M., Jeon, K. H., Tarabanis, C., Lee, S., Kim, K.-H., et al. (2026) · Korean Circulation Journal · doi:10.4070/kcj.2025.0486 · Cited by 4 · see citing works
Self-citationTadmor, T., Melamed, G., Alapi, H., & Rokach, L. (2025) · European Journal Of Haematology · doi:10.1111/ejh.14400 · Cited by 4 · see citing works
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Goyal, A., Mendoza, M., Munoz, A. E., Macias, C. A., Abou-Mrad, A. K., Marano, L., & Oviedo, R. J. (2025) · Artificial Intelligence Surgery · doi:10.20517/ais.2024.108 · Cited by 4 · see citing works
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Hsiao, Y.-C., Hung, K.-H., Yeh, C., Chen, Y., Ko, T., Lai, C.-F., Lin, L. Y., Chang, W., Tsao, Y., Lin, M. M.-W., et al. (2026) · Heart · doi:10.1136/heartjnl-2025-327462 · Cited by 2 · see citing works
Gulen, D., Gözden, H. E., Ekin, S., & Ceylan, I. (2026) · Scientific Reports · doi:10.1038/s41598-026-44576-4 · Cited by 1 · see citing works
“This aligns with broader meta-analytic observations that AI performance frequently approaches but does not surpass specialist-level interpretation 5,10-12 .”
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Champetier, A. T., López‐Ayala, P., Puelacher, C., Durak, K., Kaplan, E., Gualandro, D. M., Glarner, N., Pargger, M., Huré, G., Burri‐Winkler, K., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01983-7 · Cited by 1 · see citing works
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Xiong, K., Li, X., Ding, S., Xie, S., Li, B., Zhang, Y., & Yang, L. (2026) · Collegian Journal of the Royal College of Nursing Australia · doi:10.1016/j.colegn.2026.03.002 · Cited by 1 · see citing works
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Zhang, J., Wang, X., Qiu, H., Zhang, Y., Han, W., Wang, J., Wang, T., Shi, Y., Huang, M., Zhuang, J., et al. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125005 · Cited by 1 · see citing works
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Vrudhula, A., Stern, L. K., Cheng, P. P., Ricchiuto, P., Daluwatte, C., Witteles, R. M., Patel, J., & Ouyang, D. (2023) · medRxiv · doi:10.1101/2023.03.30.23287941 · Cited by 1 · see citing works
Self-citationSahashi, Y., Vukadinovic, M., Duffy, G., Li, D., Cheng, S. C., Berman, D. S., Ouyang, D., & Kwan, A. C. (2024) · medRxiv · doi:10.1101/2024.04.16.24305936 · Cited by 1 · see citing works
Self-citationTokodi, M., & Kovács, A. D. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.07.064 · Cited by 1 · see citing works
Harris, C., Pimpalkar, A. S., Aggarwal, A., Yang, J., Chen, X., Schmidgall, S., Rapuri, S., Greenstein, J. L., Taylor, C. O., & Stevens, R. D. (2024) · medRxiv · doi:10.1101/2024.11.19.24317577
Ong, C. S., Padrós-Valls, R., Reinertsen, E., Song, S., Young, K., Sundt, T. M., Stultz, C. M., & Aguirre, A. D. (2024) · medRxiv · doi:10.1101/2024.12.31.24319813
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Amorim, F. C., Barbosa, G. F., & Silva, D. P. D. E. (2025) · doi:10.22533/at.ed.38125160117
ASPECTOS CLÍNICOS DA TROMBOFILIA NA GESTAÇÃO
dos Santos, F. R. R., Santos, A. L. R., do Nascimento, J. O., Ribeiro, W. A., Melo, E. D. S., de Negreiros, F. M., dos Santos, M. A. F., Almeida‐Bezerra, J. W., de Lima, L. F., Cavalcante, F. N. L., et al. (2025) · doi:10.22533/at.ed.38125160116
Choi, H., Kim, Y.-R., Kim, J., Park, J., Lee, J. H., Yoon, Y. E., Oh, I., Song, I., & Cho, Y. (2025) · medRxiv · doi:10.1101/2025.05.20.25328021
Myasnikova, V. V., Aksenova, L. E., Aksenov, K. D., & Kolomytsev, V. V. (2025) · Russian Ophthalmological Journal · doi:10.21516/2072-0076-2025-18-3-supplement-27-31
Nowroozilarki, Z., Huang, S., Al-Kindi, S. G., & Mortazavi, B. J. (2025) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn66969.2025.11337742
Deep Learning Decodes Latent ECG Signatures of Stress CardiomyopathyPreprint
Ryu, J., Harris, C., Zhang, C., Gong, K. D., & Stevens, R. D. (2026) · medRxiv · doi:10.64898/2026.01.21.26344596
Chen, J., & Liu, B. H. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356844
Self-citationBorde, D. P., Joshi, S. S., Chidambaram, K., Jasapara, A., Thimmarayappa, A., Devarakonda, B. V., Kamat, S. S., Gandhe, U., Muniraj, G., Madanaik, S. S., et al. (2026) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2026.03.002
Artificial intelligence in preoperative assessment and optimization: a narrative review
Elmaleh, Y. D., Moussali, Y., & Boyer, R. B. (2026) · Türk yoğun bakım derneği dergisi/Türk yoğun bakım dergisi · doi:10.63729/tjic.2026.683
Li, X., Han, B., Ma, Q., & Yu, Y. (2026) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2026.109468
Sun, X., Zhang, C., Sun, G., Wang, P., Meng, C., & Li, Z. J. (2026) · Advanced Functional Materials · doi:10.1002/adfm.76294
Ahn, J., Jang, J. Y., Bae, J. S., Cho, Y.-H., Kang, M. G., Kim, Y.-L., Lee, H. J., Kim, K., Hwang, J., & Park, J. R. (2026) · doi:10.2196/preprints.105695
Han, S., Li, F., Hou, D., Lv, X., Zhang, H., Xiao, S., Lou, J., Li, H., Cao, J., 王迪芬, et al. (2026) · European Journal of Anaesthesiology · doi:10.1097/eja.0000000000002432
Zhuo, X.-Y., Dong, P., Lei, S.-H., Liu, B., Xu, N.-W., Jiang, S., Cai, L., Liu, K.-X., & Zhao, B.-C. (2026) · British Journal of Anaesthesia · doi:10.1016/j.bja.2026.06.025
The Role of Artificial Intelligence in Surgical Safety
Rad, A. A., Hon, J. J., Al-Omari, A., Androutsopoulou, V. A., Zotos, P.-A., & Nia, P. S. (2026) · Springer eBooks · doi:10.1007/978-3-032-26073-4_22
Guideline for the Comprehensive Perioperative Management of Older Patients With Lung Cancer (2025)
Li, D., Liang, R., Dong, B., Liu, L., & Ge, N. (2026) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.70178
Li, J., Fang, X., Yu, P., & Jiang, L. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1913206
Šoltesová, K., & Astapenko, D. (2026) · Anesteziologie a intenzivní medicína · doi:10.36290/aim.2026.030
Advancing Perioperative Medicine With AI: Current Landscape and Research Priorities
Rohatgi, N., & Mathur, P. (2026) · JMIR Perioperative Medicine · doi:10.2196/107155
- Authors
- Ouyang, D., Theurer, J., Stein, N. R., Hughes, J. W., Elias, P., He, B., Yuan, N., Duffy, G., Sandhu, R. K., Ebinger, J., Botting, P., Jujjavarapu, M., Claggett, B., Tooley, J. E., Poterucha, T., Chen, J. H., Nurok, M., Perez, M., Perotte, A., Zou, J. Y., Cook, N. R., Chugh, S. S., Cheng, S., & Albert, C. M.
- Affiliations
- Cedars-Sinai Medical Center (US)
- Stanford University (US)
- Columbia University Irving Medical Center (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Ouyang, David
- ORCID
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 6
- Issue
- 1
- Pages
- e70-e78
- ISSN
- 2589-7500 (ISSN-L)
- PMC
- PMC13184298
- OpenAlex
- W4389425951
- Cited by
- 64
- References
- 29
- Citations by year
- 2026: 22 · 2025: 24 · 2024: 14 · 2023: 4
- FWCI
- 12.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R01HL136390, K99HL157421, R01HL165840, R00HL157421, T32HL007854)
- National Institute on Drug Abuse (UG1DA015815)
- National Institute of Allergy and Infectious Diseases (R01AI178121)
- U.S. National Library of Medicine (R56LM013365)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- electrocardiogram, deep learning, post-procedural mortality, preoperative risk assessment, prognostic model, 12-lead ECG, Revised Cardiac Risk Index, external validation, medical procedures, mortality prediction, ECG waveform analysis, risk stratification, length of hospital stay, hospital costs
- MeSH terms
- Deep Learning, Algorithms, Electrocardiography, Humans, Prognosis, Risk Assessment
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