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

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.

Prognostic Performance of Sequential Organ Failure Assessment, Acute Physiology and Chronic Health Evaluation III, and Simplified Acute Physiology Score II Scores in Patients with Suspected Infection According to Intensive Care Unit Type

“…Our study focused on widely used, rule-based models in intensive care for future modification and development. Machine-learning-based prediction models have been developed in emergency and critical care settings and can be considered among the solutions for improving the prognostic performance across ICU types, although there are still barriers for adopting machine-learning models in clinical practice [36,37].…”

Use of artificial intelligence in critical care: opportunities and obstacles

“…Presently, only few AI-based algorithms have shown evidence for improved clinician performance or patient outcomes in clinical studies [ 6 , 16 , 17 ]. Reasons proposed for this so-called AI chasm [ 18 ] are lack of necessary expertise needed for translating a tool into practice, lack of funding available for translation, underappreciation of clinical research as a translation mechanism, disregard for the potential value of the early stages of clinical evaluation and the analysis of human factors [ 19 ], and poor reporting and evaluations [ 2 , 8 , 20 ].…”

Explainable artificial intelligence in emergency medicine: an overview

“…Artificial intelligence (AI) and machine learning (ML) are powerful technologies that have the potential to improve medical care [ 1 ]. AI refers to the broader concept of technology being able to carry out tasks in an autonomous and smart way, encompassing a variety of technologies, while ML is a subset of AI focused on the idea that machines can learn from data, identify patterns, and make decisions with minimal human intervention [ 1 – 4 ]. Particularly in emergency medicine, AI and ML are expected to play critical roles in accelerating triage, diagnosis, and prognostication to optimize individual patient care through the input of clinical information and/or image recognition [ 2 , 4 – 8 ].…”

“…AI refers to the broader concept of technology being able to carry out tasks in an autonomous and smart way, encompassing a variety of technologies, while ML is a subset of AI focused on the idea that machines can learn from data, identify patterns, and make decisions with minimal human intervention [ 1 – 4 ]. Particularly in emergency medicine, AI and ML are expected to play critical roles in accelerating triage, diagnosis, and prognostication to optimize individual patient care through the input of clinical information and/or image recognition [ 2 , 4 – 8 ]. Furthermore, streamlined clinical documentation or recording using natural language processing is expected to make these tasks more efficient [ 9 – 11 ].…”