Artificial intelligence for sepsis management in the emergency department

Abstract

Sepsis a major cause of emergency department (ED) presentations, with over 850,000 cases annually in the United States alone. Despite decades of research and numerous clinical trials, sepsis management still relies on the basic principles of infection control and organ support, with no sepsis-specific therapies proving universally effective. Recent advances in artificial intelligence (AI) offer new opportunities to address this challenge by supporting clinicians in early detection, optimizing diagnostic work-ups, and improving treatment decisions. This narrative review highlights the potential of AI tools in sepsis care, particularly in the ED setting. Early detection systems have demonstrated potential to alert clinicians to sepsis onset, but concerns about their real-world performance and impact remain. Diagnostic innovations, such as AI tools predicting blood culture positivity or novel biomarker-integrated platforms like the Sepsis ImmunoScore, promise to enhance accuracy and efficiency in identifying infections. Furthermore, AI-driven patient stratification into clinically or biologically defined subgroups, enables a precision medicine approach to sepsis treatment, identifying patients most likely to benefit from specific interventions. Despite these advances, significant challenges remain. Most AI tools for sepsis have been validated only in observational studies, with limited randomized evidence to support their clinical utility. Moreover, the heterogeneous and dynamic nature of sepsis complicates the development and implementation of AI tools in the ED, where data capture is often incomplete. Additional barriers include issues of generalizability, explainability, and the need for patient involvement in AI development and deployment. As AI continues to evolve, its potential to transform sepsis management in the ED is clear. However, rigorous clinical trials and thoughtful integration into existing workflows are essential to ensure that AI-driven tools meaningfully improve outcomes for patients with sepsis.

Ähnliche Arbeiten