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Purpose of Review <p>This paper examines the potential of artificial intelligence (AI) algorithms to improve rates of early sepsis detection compared to traditional scoring methods, exploring AI integration challenges, clinical performance, and ethical considerations.</p> Recent Findings <p>Recent studies demonstrate that AI models including SERA, TREWS, COMPOSER, and Sepsis ImmunoScore™ outperform traditional scoring tools such as SOFA, qSOFA, SIRS, and MEWS in sensitivity, specificity, and early detection. These algorithms utilize a variety of clinical variables and through integration with electronic medical records (EMR) can reduce hospital stay, organ failure, and mortality.</p> Summary <p>The use of AI for early detection shows promise for improving sepsis-related outcomes, although widespread adoption is challenged by barriers including physician trust, patient privacy, environmental impact, and bias mitigation. Future research is needed, including multicenter validation, optimization of predictive variables, bias mitigation, and studies of environmental impact.</p>

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The Use of Artificial Intelligence for the Early Detection of Sepsis in the Emergency Department

  • 期刊论文

  • 发表日期：

    2025年11月28日

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  • Noor Ghanam,
  • James Paxton

摘要

Purpose of Review

This paper examines the potential of artificial intelligence (AI) algorithms to improve rates of early sepsis detection compared to traditional scoring methods, exploring AI integration challenges, clinical performance, and ethical considerations.

Recent Findings

Recent studies demonstrate that AI models including SERA, TREWS, COMPOSER, and Sepsis ImmunoScore™ outperform traditional scoring tools such as SOFA, qSOFA, SIRS, and MEWS in sensitivity, specificity, and early detection. These algorithms utilize a variety of clinical variables and through integration with electronic medical records (EMR) can reduce hospital stay, organ failure, and mortality.

Summary

The use of AI for early detection shows promise for improving sepsis-related outcomes, although widespread adoption is challenged by barriers including physician trust, patient privacy, environmental impact, and bias mitigation. Future research is needed, including multicenter validation, optimization of predictive variables, bias mitigation, and studies of environmental impact.