Artificial intelligence to advance acute and intensive care medicine

被引:9
作者
Biesheuvel, Laurens A. [1 ,2 ]
Dongelmans, Dave A. [3 ,4 ]
Elbers, Paul W. G. [1 ]
机构
[1] Amsterdam UMC, Ctr Crit Care Computat Intelligence C4I, Dept Intens Care Med,Amsterdam Inst Infect & Immu, Amsterdam Med Data Sci AMDS,Amsterdam Cardiovasc, Amsterdam, Netherlands
[2] Vrije Univ, Dept Comp Sci, Quantitat Data Analyt Grp, Fac Sci, Amsterdam, Netherlands
[3] Univ Amsterdam, Dept Intens Care Med, Amsterdam Publ Hlth APH, Amsterdam UMC, Amsterdam, Netherlands
[4] Natl Intens Care Evaluat Fdn, Amsterdam, Netherlands
关键词
artificial intelligence; generative artificial intelligence; machine learning; predictive models;
D O I
10.1097/MCC.0000000000001150
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
Purpose of review This review explores recent key advancements in artificial intelligence for acute and intensive care medicine. As artificial intelligence rapidly evolves, this review aims to elucidate its current applications, future possibilities, and the vital challenges that are associated with its integration into emergency medical dispatch, triage, medical consultation and ICUs. Recent findings The integration of artificial intelligence in emergency medical dispatch (EMD) facilitates swift and accurate assessment. In the emergency department (ED), artificial intelligence driven triage models leverage diverse patient data for improved outcome predictions, surpassing human performance in retrospective studies. Artificial intelligence can streamline medical documentation in the ED and enhances medical imaging interpretation. The introduction of large multimodal generative models showcases the future potential to process varied biomedical data for comprehensive decision support. In the ICU, artificial intelligence applications range from early warning systems to treatment suggestions. Summary Despite promising academic strides, widespread artificial intelligence adoption in acute and critical care is hindered by ethical, legal, technical, organizational, and validation challenges. Despite these obstacles, artificial intelligence's potential to streamline clinical workflows is evident. When these barriers are overcome, future advancements in artificial intelligence have the potential to transform the landscape of patient care for acute and intensive care medicine.
引用
收藏
页码:246 / 250
页数:5
相关论文
共 25 条
[11]  
Ivanov O., 2022, Detection of sepsis during emergency department triage using machine learning
[12]   I MPROVING ED E MERGENCY S EVERITY I NDEX A CUITY A SSIGNMENT U SING M ACHINE L EARNING AND C LINICAL N ATURAL L ANGUAGE P ROCESSING [J].
Ivanov, Oleksandr ;
Wolf, Lisa ;
Brecher, Deena ;
Lewis, Erica ;
Masek, Kevin ;
Montgomery, Kyla ;
Andrieiev, Yurii ;
McLaughlin, Moss ;
Liu, Stephen ;
Dunne, Robert ;
Klauer, Kevin ;
Reilly, Christian .
JOURNAL OF EMERGENCY NURSING, 2021, 47 (02)
[13]  
Johnson AEW, 2023, SCI DATA, V10, DOI 10.1038/s41597-022-01899-x
[14]  
Ministry of Health and Family Welfare, 2020, Machine Learning: A Bayesian and Optimization Perspective, P1, DOI [DOI 10.1016/C2019-0-03772-7, https://doi.org/10.1016/B978-0-12-818612-1]
[15]  
OpenAI, ChatGPT
[16]   Does Reinforcement Learning Improve Outcomes for Critically Ill Patients? A Systematic Review and Level-of-Readiness Assessment [J].
Otten, Martijn ;
Jagesar, Ameet R. ;
Dam, Tariq A. ;
Biesheuvel, Laurens A. ;
den Hengst, Floris ;
Ziesemer, Kirsten A. ;
Thoral, Patrick J. ;
de Grooth, Harm-Jan ;
Girbes, Armand R. J. ;
Francois-Lavet, Vincent ;
Hoogendoorn, Mark ;
Elbers, Paul W. G. .
CRITICAL CARE MEDICINE, 2024, 52 (02) :e79-e88
[17]   The eICU Collaborative Research Database, a freely available multi-center database for critical care research [J].
Pollard, Tom J. ;
Johnson, Alistair E. W. ;
Raffa, Jesse D. ;
Celi, Leo A. ;
Mark, Roger G. ;
Badawi, Omar .
SCIENTIFIC DATA, 2018, 5
[18]  
Shimabukuro DW, 2017, BMJ OPEN RESPIR RES, V4, DOI 10.1136/bmjresp-2017-000234
[19]   Machine learning in medicine: a practical introduction [J].
Sidey-Gibbons, Jenni A. M. ;
Sidey-Gibbons, Chris J. .
BMC MEDICAL RESEARCH METHODOLOGY, 2019, 19 (1)
[20]  
Thoral Patrick J, 2021, Crit Care Explor, V3, pe0529, DOI 10.1097/CCE.0000000000000529