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Related Experiment Video

Updated: Sep 11, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

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Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management.

Georgios I Barkas1, Ilias E Dimeas2, Ourania S Kotsiou1,2

  • 1Laboratory of Human Pathophysiology, Department of Nursing, School of Health Sciences, University of Thessaly, 41500 Larissa, Greece.

Diagnostics (Basel, Switzerland)
|August 14, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) show promise in improving sepsis care through early detection and personalized antibiotic management. Further research is needed for real-world validation and ethical implementation to maximize clinical impact.

Keywords:
artificial intelligencemachine learningpredictionsepsis

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Clinical Decision Support Systems

Background:

  • Sepsis is a major global health challenge, leading to high mortality rates.
  • Delayed diagnosis and overuse of antibiotics contribute to poor patient outcomes and antimicrobial resistance.
  • Current sepsis management requires innovative solutions for early detection and optimized treatment.

Purpose of the Study:

  • To systematically review artificial intelligence (AI) and machine learning (ML) applications in sepsis care.
  • To evaluate AI/ML for early detection, personalized antibiotic management, and antimicrobial resistance forecasting.
  • To identify challenges and future directions for AI/ML in sepsis management.

Main Methods:

  • Systematic scoping review of literature from 2019 to 2025.
  • Adherence to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.
  • Quality assessment using the Joanna Briggs Institute (JBI) and QUADAS-2 tools.

Main Results:

  • AI/ML models demonstrated strong predictive performance for early sepsis detection (AUROC 0.68-0.99).
  • AI tools like InSight and KI.SEP utilize multimodal data for real-time clinical support.
  • AI platforms show potential in reducing inappropriate antibiotic use and improving patient outcomes.

Conclusions:

  • AI and ML offer significant potential to revolutionize sepsis care, enhancing early detection and treatment.
  • Challenges include data integration, algorithmic bias, interpretability, and the need for real-world validation.
  • Future efforts must focus on multicenter validation, seamless clinical workflow integration, and ethical frameworks for effective deployment.