The premise, promise, and perils of artificial intelligence in critical care cardiology

被引：0

作者：

Huerta, Nicholas ^{[1
]}

Rao, Shiavax J. ^{[1
]}

Isath, Ameesh ^{[2
,3
]}

Wang, Zhen ^{[4
,5
]}

Glicksberg, Benjamin S. ^{[6
]}

Krittanawong, Chayakrit ^{[7
,8
]}

机构：

[1] MedStar Union Mem Hosp, Dept Med, Baltimore, MD USA

[2] Westchester Med Ctr, Dept Cardiol, Valhalla, NY USA

[3] New York Med Coll, Valhalla, NY USA

[4] Mayo Clin, Robert D & Patricia E Kern Ctr Sci Hlth Care Deliv, Rochester, MN USA

[5] Mayo Clin, Dept Hlth Sci Res, Div Hlth Care Policy & Res, Rochester, MN USA

[6] Icahn Sch Med Mt Sinai, Hasso Plattner Inst Digital Hlth, New York, NY USA

[7] NYU Langone Hlth, Cardiol Div, New York, NY USA

[8] Sch Med, NYU, New York, NY USA

来源：

PROGRESS IN CARDIOVASCULAR DISEASES | 2024年 / 86卷

关键词：

Artificial intelligence; Critical care; Cardiac; Intensive care; Cardiogenic shock; INFECTIONS; MORTALITY;

D O I：

10.1016/j.pcad.2024.06.006

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

Artificial intelligence (AI) is an emerging technology with numerous healthcare applications. AI could prove particularly useful in the cardiac intensive care unit (CICU) where its capacity to analyze large datasets in realtime would assist clinicians in making more informed decisions. This systematic review aimed to explore current research on AI as it pertains to the CICU. A PRISMA search strategy was carried out to identify the pertinent literature on topics including vascular access, heart failure care, circulatory support, cardiogenic shock, ultrasound, and mechanical ventilation. Thirty-eight studies were included. Although AI is still in its early stages of development, this review illustrates its potential to yield numerous benefits in the CICU.

引用

页码：2 / 12

页数：11

共 61 条

[1]

Adlassnig Klaus-Peter, 2009, Stud Health Technol Inform, V149, P103

[2] Fully Automated Surveillance of Healthcare-Associated Infections with MONI-ICU A Breakthrough in Clinical Infection Surveillance [J].

Blacky, A. ;

Mandl, H. ;

Adlassnig, K-P ;

Koller, W. .

APPLIED CLINICAL INFORMATICS, 2011, 2 (03) :365-372

[3] Artificial intelligence versus expert: a comparison of rapid visual inferior vena cava collapsibility assessment between POCUS experts and a deep learning algorithm [J].

Blaivas, Michael ;

Adhikari, Srikar ;

Savitsky, Eric A. ;

Blaivas, Laura N. ;

Liu, Yiju T. .

JOURNAL OF THE AMERICAN COLLEGE OF EMERGENCY PHYSICIANS OPEN, 2020, 1 (05) :857-864

[4] Development of a Deep Learning Network to Classify Inferior Vena Cava Collapse to Predict Fluid Responsiveness [J].

Blaivas, Michael ;

Blaivas, Laura ;

Philips, Gary ;

Merchant, Roland ;

Levy, Mitchell ;

Abbasi, Adeel ;

Eickhoff, Carsten ;

Shapiro, Nathan ;

Corl, Keith .

JOURNAL OF ULTRASOUND IN MEDICINE, 2021, 40 (08) :1495-1504

[5] AI-Enabled, Ultrasound-Guided Handheld Robotic Device for Femoral Vascular Access [J].

Brattain, Laura J. ;

Pierce, Theodore T. ;

Gjesteby, Lars A. ;

Johnson, Matthew R. ;

DeLosa, Nancy D. ;

Werblin, Joshua S. ;

Gupta, Jay F. ;

Ozturk, Arinc ;

Wang, Xiaohong ;

Li, Qian ;

Telfer, Brian A. ;

Samir, Anthony E. .

BIOSENSORS-BASEL, 2021, 11 (12)

[6] The impact of electronic health records on healthcare quality: a systematic review and meta-analysis [J].

Campanella, Paolo ;

Lovato, Emanuela ;

Marone, Claudio ;

Fallacara, Lucia ;

Mancuso, Agostino ;

Ricciardi, Walter ;

Specchia, Maria Lucia .

EUROPEAN JOURNAL OF PUBLIC HEALTH, 2016, 26 (01) :60-64

[7]

CDC, CDC Clinical Standardization Programs

[8] An artificial intelligence tool to predict fluid requirement in the intensive care unit: a proof-of-concept study [J].

Celi, Leo Anthony ;

Hinske, L. Christian ;

Alterovitz, Gil ;

Szolovits, Peter .

CRITICAL CARE, 2008, 12 (06) :R151

[9] Predictors of in-ICU length of stay among congenital heart defect patients using artificial intelligence model: A pilot study [J].

Chang Junior, Joao ;

Caneo, Luiz Fernando ;

Turquetto, Aida Luiza Ribeiro ;

Amato, Luciana Patrick ;

Arita, Elisandra Cristina Trevisan Calvo ;

Fernandes, Alfredo Manoel da Silva ;

Trindade, Evelinda Marramon ;

Jatene, Fabio Biscegli ;

Dossou, Paul-Eric ;

Jatene, Marcelo Biscegli .

HELIYON, 2024, 10 (04)

[10] Early Prediction of Cardiogenic Shock Using Machine Learning [J].

Chang, Yale ;

Antonescu, Corneliu ;

Ravindranath, Shreyas ;

Dong, Junzi ;

Lu, Mingyu ;

Vicario, Francesco ;

Wondrely, Lisa ;

Thompson, Pam ;

Swearingen, Dennis ;

Acharya, Deepak .

FRONTIERS IN CARDIOVASCULAR MEDICINE, 2022, 9

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