Artificial Intelligence- and Machine Learning-Assisted Subphenotyping for Personalized Immunotherapy in Sepsis

TL;DR

This paper reviews how AI and machine learning can help identify different types of sepsis to improve personalized immunotherapy treatments.

Contribution

The paper provides a narrative review of AI/ML-based subphenotyping in sepsis and its implications for immunotherapy response.

Findings

AI/ML algorithms identified sepsis subphenotypes using clinical and biological data.

Hyper- and hypoinflammatory subphenotypes showed different responses to immune modulating therapies like statins and corticosteroids.

Abstract

Background/Objectives: Sepsis heterogeneity limits advances in immunotherapy. Increasing use of artificial intelligence (AI) and machine learning (ML) attempts to turn multi-dimensional data into meaningful clusters, indicating biological mechanisms. We provide an overview of the existing evidence on AI-derived sepsis subtyping, exploring treatment response to available immune modulating therapies. Methods: On 1 October 2025, we conducted a structured search on all relative publications on MEDLINE and undertook a narrative review. Results: Multiple subphenotyping algorithms were identified, using clinical, biological, and omics data, across different cohorts, mainly through secondary analyses of randomized trials. The main classification was between hyper- and hypoinflammatory subphenotypes. Statins, corticosteroids, activated protein C, or thrombomodulin displayed differential effects on the outcome of these subphenotypes. Conclusions: Further research is required to prospectively validate findings and to offer pragmatic solutions to patients who need them the most. Issues of validity, equity, ethics, and feasibility are discussed.

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