Mui Teng Chua, Yuru Boon, Zi Yao Lee, Jian Hao Jaryl Kok, Clement Kee Woon Lim, Nicole Mun Teng Cheung, Lorraine Pei Xian Yong, Win Sen Kuan
{"title":"The role of artificial intelligence in sepsis in the Emergency Department: a narrative review.","authors":"Mui Teng Chua, Yuru Boon, Zi Yao Lee, Jian Hao Jaryl Kok, Clement Kee Woon Lim, Nicole Mun Teng Cheung, Lorraine Pei Xian Yong, Win Sen Kuan","doi":"10.21037/atm-24-150","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and objective: </strong>Early recognition and treatment of sepsis in the emergency department (ED) is important. Traditional predictive analytics and clinical decision rules lack accuracy in identifying patients with sepsis. Artificial intelligence (AI) is increasingly prevalent in healthcare and offers application potential in the care of patients with sepsis. This review examines the evidence of AI in diagnosing, managing and prognosticating sepsis in the ED.</p><p><strong>Methods: </strong>We performed literature search in PubMed, Embase, Google Scholar and Scopus databases for studies published between 1 January 2010 and 30 June 2024 that evaluated the use of AI in adult patients with sepsis in ED, using the following search terms: (\"artificial intelligence\" OR \"machine learning\" OR \"neural networks, computer\" OR \"deep learning\" OR \"natural language processing\"), AND (\"sepsis\" OR \"septic shock\", AND \"emergency services\" OR \"emergency department\"). Independent searches were conducted in duplicate with discrepancies adjudicated by a third member.</p><p><strong>Key content and findings: </strong>Incorporating multiple variables such as vital signs, free text input, laboratory tests and electrocardiogram was possible with AI compared to traditional models leading to improvement in diagnostic performance. Machine learning (ML) models outperformed traditional scoring tools in both diagnosis and prognosis of sepsis. ML models were able to analyze trends over time and showed utility in predicting mortality, severe sepsis and septic shock. Additionally, real-time ML-assisted alert systems are effective in improving time-to-antibiotic administration and ML algorithms can differentiate sepsis patients into distinct phenotypes to tailor management (especially fluid therapy and critical care interventions), potentially improving outcomes. Existing AI tools for sepsis currently lack generalizability and user acceptance. This is risk of automation bias with loss of clinicians' skills if over-reliance develops.</p><p><strong>Conclusions: </strong>Overall, AI holds great promise in revolutionizing management of patients with sepsis in the ED as a clinical support tool. However, its application is currently still constrained by inherent limitations. Balanced integration of AI technology with clinician input is essential to harness its full potential and ensure optimal patient outcomes.</p>","PeriodicalId":8216,"journal":{"name":"Annals of translational medicine","volume":"13 1","pages":"4"},"PeriodicalIF":0.0000,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11921180/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of translational medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/atm-24-150","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/25 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background and objective: Early recognition and treatment of sepsis in the emergency department (ED) is important. Traditional predictive analytics and clinical decision rules lack accuracy in identifying patients with sepsis. Artificial intelligence (AI) is increasingly prevalent in healthcare and offers application potential in the care of patients with sepsis. This review examines the evidence of AI in diagnosing, managing and prognosticating sepsis in the ED.
Methods: We performed literature search in PubMed, Embase, Google Scholar and Scopus databases for studies published between 1 January 2010 and 30 June 2024 that evaluated the use of AI in adult patients with sepsis in ED, using the following search terms: ("artificial intelligence" OR "machine learning" OR "neural networks, computer" OR "deep learning" OR "natural language processing"), AND ("sepsis" OR "septic shock", AND "emergency services" OR "emergency department"). Independent searches were conducted in duplicate with discrepancies adjudicated by a third member.
Key content and findings: Incorporating multiple variables such as vital signs, free text input, laboratory tests and electrocardiogram was possible with AI compared to traditional models leading to improvement in diagnostic performance. Machine learning (ML) models outperformed traditional scoring tools in both diagnosis and prognosis of sepsis. ML models were able to analyze trends over time and showed utility in predicting mortality, severe sepsis and septic shock. Additionally, real-time ML-assisted alert systems are effective in improving time-to-antibiotic administration and ML algorithms can differentiate sepsis patients into distinct phenotypes to tailor management (especially fluid therapy and critical care interventions), potentially improving outcomes. Existing AI tools for sepsis currently lack generalizability and user acceptance. This is risk of automation bias with loss of clinicians' skills if over-reliance develops.
Conclusions: Overall, AI holds great promise in revolutionizing management of patients with sepsis in the ED as a clinical support tool. However, its application is currently still constrained by inherent limitations. Balanced integration of AI technology with clinician input is essential to harness its full potential and ensure optimal patient outcomes.
期刊介绍:
The Annals of Translational Medicine (Ann Transl Med; ATM; Print ISSN 2305-5839; Online ISSN 2305-5847) is an international, peer-reviewed Open Access journal featuring original and observational investigations in the broad fields of laboratory, clinical, and public health research, aiming to provide practical up-to-date information in significant research from all subspecialties of medicine and to broaden the readers’ vision and horizon from bench to bed and bed to bench. It is published quarterly (April 2013- Dec. 2013), monthly (Jan. 2014 - Feb. 2015), biweekly (March 2015-) and openly distributed worldwide. Annals of Translational Medicine is indexed in PubMed in Sept 2014 and in SCIE in 2018. Specific areas of interest include, but not limited to, multimodality therapy, epidemiology, biomarkers, imaging, biology, pathology, and technical advances related to medicine. Submissions describing preclinical research with potential for application to human disease, and studies describing research obtained from preliminary human experimentation with potential to further the understanding of biological mechanism underlying disease are encouraged. Also warmly welcome are studies describing public health research pertinent to clinic, disease diagnosis and prevention, or healthcare policy. With a focus on interdisciplinary academic cooperation, ATM aims to expedite the translation of scientific discovery into new or improved standards of management and health outcomes practice.