![]() ![]() This study mainly focuses on recent research regarding 1) the purposes of AI use in emergency care, 2) how AI is used in EDs, 3) the effects of AI on ED’s functioning, and 4) the effects on the work design ED clinicians. To advance our understanding of AI use in emergency medicine, this paper provides a systematic literature review to examine the effects of AI on the work design of emergency clinicians. While research on AI’s clinical utility is increasing, no studies assess the influence of AI clinical decision support tools on ED clinician behavior and patient flow. At the same time, physicians focus on the more challenging aspects of the job, such as communication with professionals and patients. The less complex tasks, such as interpreting images, could be unraveled with AI. Nevertheless, because the thinking ability of AI exceeds human capacity and pace, AI can alter the role of the emergency physician. In emergency medicine, speed is essential, so a computer's quick “brain” could be used in such an environment. Īlthough some argue that AI might eventually take over some of the work of emergency personnel, such as radiologists, the evidence currently shows that AI can significantly improve the quality and speed of emergency medicine. This makes AI even more helpful, as it has shown high accuracy in addition to speed. However, this method relies heavily on subjective data, which makes it prone to errors. One of the current standard methods to achieve this is the Emergency Severity Index (ESI) assistance, which helps triage patients at high speed. In emergency departments (EDs), a fast interpretation of clinical data to categorize the severity of patients’ conditions is of great importance. One particular strength of AI, the speed with which it can make inferences, makes it relevant for emergency medicine. For example, a prediction model using ML can recognize heart rate and blood pressure patterns, which can help detect sepsis at an earlier stage, significantly improving patient outcomes. ML can improve algorithms by recognizing patterns in large numbers of data and can make calculations or predictions using statistical approaches. ![]() ![]() There are subdivisions in kinds of AI technology, and one of them is machine learning. While “AI” is often understood as either complex and all-encompassing or vague, it comes down to a computer that simulates human intelligence by learning to make deductions when fed new data. ĪI is increasingly used in healthcare as it can work as a catalyst to overcome significant challenges of health systems. AI applications aim to comprehend and develop electronic methods that embed intelligence properties. It is the concept of computer systems performing tasks that usually demands human knowledge. AI is machine-learned intelligence instead of the natural intelligence humans or animals display. Healthcare providers globally recognize that part of the solution to these challenges is to embed artificial intelligence (AI) into their work processes. To respond to these challenges, healthcare must continue to improve its productivity and efficiency, which raises the question of whether healthcare professionals' expectations to deliver good healthcare might still be within human capabilities. Healthcare faces significant challenges of a “rising burden of illness, multimorbidity and disability driven by aging and epidemiological transition, greater demand for health services, higher societal expectations, and increasing health expenditures”. Over the past years, the need for a higher quality of care has increased significantly. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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