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. 2023 Jun;10(2):132-137.
doi: 10.15441/ceem.23.041. Epub 2023 May 15.

Current challenges in adopting machine learning to critical care and emergency medicine

Cyra-Yoonsun Kang  1 , Joo Heung Yoon  2
Affiliations

Current challenges in adopting machine learning to critical care and emergency medicine

Cyra-Yoonsun Kang et al. Clin Exp Emerg Med. 2023 Jun.

Abstract

Over the past decades, the field of machine learning (ML) has made great strides in medicine. Despite the number of ML-inspired publications in the clinical arena, the results and implications are not readily accepted at the bedside. Although ML is very powerful in deciphering hidden patterns in complex critical care and emergency medicine data, various factors including data, feature generation, model design, performance assessment, and limited implementation could affect the utility of the research. In this short review, a series of current challenges of adopting ML models to clinical research will be discussed.

Keywords: Artificial intelligence; Challenges; Critical care; Machine learning.

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Conflict of interest statement

CONFLICT OF INTEREST

No potential conflict of interest relevant to this article was reported.

Figures

Fig. 1.
Fig. 1.
Challenges in adopting machine learning (ML).
See this image and copyright information in PMC

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