Artificial Intelligence in Emergency Medicine: Viewpoint of Current Applications and Foreseeable Opportunities and Challenges

被引:38
作者
Chenais, Gabrielle [1 ]
Lagarde, Emmanuel [1 ]
Gil-Jardine, Cedric [1 ,2 ]
机构
[1] Bordeaux Populat Hlth Ctr, INSERM U1219, Bordeaux, France
[2] Bordeaux Univ Hosp, Bordeaux, France
关键词
viewpoint; ethics; artificial intelligence; emergency medicine; perspectives; mobile phone; PATIENT SATISFACTION; HEALTH-CARE; WAITING TIME; ASSOCIATION; TRIAGE; INFORMATION; MORTALITY; IMPACT; DETERMINANTS; DEPARTMENTS;
D O I
10.2196/40031
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Emergency medicine and its services have reached a breaking point during the COVID-19 pandemic. This pandemic has highlighted the failures of a system that needs to be reconsidered, and novel approaches need to be considered. Artificial intelligence (AI) has matured to the point where it is poised to fundamentally transform health care, and applications within the emergency field are particularly promising. In this viewpoint, we first attempt to depict the landscape of AI-based applications currently in use in the daily emergency field. We review the existing AI systems; their algorithms; and their derivation, validation, and impact studies. We also propose future directions and perspectives. Second, we examine the ethics and risk specificities of the use of AI in the emergency field.
引用
收藏
页数:19
相关论文
共 142 条
[21]  
babylonhealth / neuralTPPs, GITHUB
[22]   Machine learning in the prediction of medical inpatient length of stay [J].
Bacchi, Stephen ;
Tan, Yiran ;
Oakden-Rayner, Luke ;
Jannes, Jim ;
Kleinig, Timothy ;
Koblar, Simon .
INTERNAL MEDICINE JOURNAL, 2022, 52 (02) :176-185
[23]   A Comparison of Artificial Intelligence and Human Doctors for the Purpose of Triage and Diagnosis [J].
Baker, Adam ;
Perov, Yura ;
Middleton, Katherine ;
Baxter, Janie ;
Mullarkey, Daniel ;
Sangar, Davinder ;
Butt, Mobasher ;
DoRosario, Arnold ;
Johri, Saurabh .
FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2020, 3
[24]   Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI [J].
Barredo Arrieta, Alejandro ;
Diaz-Rodriguez, Natalia ;
Del Ser, Javier ;
Bennetot, Adrien ;
Tabik, Siham ;
Barbado, Alberto ;
Garcia, Salvador ;
Gil-Lopez, Sergio ;
Molina, Daniel ;
Benjamins, Richard ;
Chatila, Raja ;
Herrera, Francisco .
INFORMATION FUSION, 2020, 58 :82-115
[25]  
Bellika J, 2015, DIGITAL HEALTHCARE E
[26]   On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? [J].
Bender, Emily M. ;
Gebru, Timnit ;
McMillan-Major, Angelina ;
Shmitchell, Shmargaret .
PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, 2021, :610-623
[27]   SIMPSONS PARADOX AND SURE-THING PRINCIPLE [J].
BLYTH, CR .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1972, 67 (33) :364-&
[28]   Do End-to-End Speech Recognition Models Care About Context? [J].
Borgholt, Lasse ;
Havtorn, Jakob D. ;
Agic, Zeljko ;
Sogaard, Anders ;
Maaloe, Lars ;
Igel, Christian .
INTERSPEECH 2020, 2020, :4352-4356
[29]  
Boulos Maged N Kamel, 2014, Online J Public Health Inform, V5, P229, DOI 10.5210/ojphi.v5i3.4814
[30]  
Bozkurt Biykem, 2017, Methodist Debakey Cardiovasc J, V13, P216, DOI 10.14797/mdcj-13-4-216