Artificial Intelligence in Sepsis Management: An Overview for Clinicians

Area of Science:

• Medical Informatics
• Clinical Decision Support Systems
• Artificial Intelligence in Healthcare

Background:

• Sepsis is a leading cause of hospital mortality, necessitating early diagnosis for better outcomes.
• Artificial intelligence (AI) and machine learning (ML) are increasingly explored for sepsis prediction, diagnosis, and treatment personalization.
• ML models utilize clinical data for early sepsis risk prediction, hours before symptom onset.

Purpose of the Study:

• To review the application of AI in sepsis management.
• To provide an overview of studies on AI in sepsis, assessing effectiveness, limitations, and future potential.
• To guide clinicians and healthcare professionals on AI's role in sepsis.

Main Methods:

• Systematic review adhering to the SPIDER framework.
• Critical overview by three reviewers, selecting original and comprehensive data.
• Analysis of selected articles based on focus: early prediction, diagnosis, mortality, or treatment improvement.

Main Results:

• 194 articles were identified, with 28 selected for review.
• AI/ML applications in sepsis show mixed results: some studies report improved mortality and management.
• Challenges include high false positive rates and insufficient external validation in existing research.

Conclusions:

• AI and ML offer potential for enhancing sepsis care, but further validation is crucial.
• Addressing limitations like false positives is key to realizing AI's full potential in sepsis management.
• Continued research and development are needed to optimize AI tools for clinical sepsis care.