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Updated: Sep 11, 2025
07:42
A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
315
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
Summary
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.
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.
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