Key result
AI models show promise for predicting critical care sepsis, ARDS, and mortality pending prospective validation.
Why the study?
With newer studies testing and validating AI models to improve patient care strategies, a review was needed to summarize current literature and analyze its future utility in critical care medicine.
Do artificial intelligence and machine learning models improve the prediction and management of complications in critically ill patients?
Do artificial intelligence and machine learning models improve the prediction and management of complications in critically ill patients?
Artificial intelligence and machine learning models show promising potential to accurately predict critical care complications earlier than traditional methods, though large-scale prospective validation is required before routine clinical implementation.
May aid earlier ICU risk stratification; leaves open prospective validation before clinical adoption.
We have witnessed rapid advancement in technology over the last few decades. With the advent of artificial intelligence (AI), newer avenues have opened for researchers. AI has added an entirely new dimension to this technological boom. Researchers in medical science have been excited about the tantalizing prospect of utilizing AI for the benefit of patient care. Lately, we have come across studies trying to test and validate various models based on AI to improve patient care strategies in critical care medicine as well. Thus, in this review, we will attempt to succinctly review current literature discussing AI in critical care medicine and analyze its future utility based on prevailing evidence.
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Sharma et al. (2021) conducted a review in Critical illness. Artificial Intelligence and Machine Learning vs. Standard clinical care and traditional scoring systems was evaluated. Artificial intelligence and machine learning models demonstrate potential in critical care for early prediction of sepsis, ARDS, and mortality, though large-scale prospective validation remains necessary.
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