Transforming sepsis management: AI-driven innovations in early detection and tailored therapies

Abstract

Sepsis remains a leading cause of mortality worldwide, driven by its clinical complexity and delayed recognition. Artificial intelligence (AI) offers promising solutions to improve sepsis care through earlier detection, risk stratification, and personalized treatment strategies. Key applications include AI-driven early warning systems, subphenotyping based on clinical and biological data, and decision support tools that adapt to real-time patient information. The integration of diverse data types, such as structured clinical data, unstructured notes, waveform signals, and molecular biomarkers, enhances the precision and timeliness of interventions. However, challenges such as algorithmic bias, limited external validation, data quality issues, and ethical considerations continue to hinder clinical implementation. Future directions focus on real-time model adaptation, multi-omics integration, and the development of generalist medical AI capable of personalized recommendations. Successfully addressing these barriers is essential for AI to deliver on its potential to transform sepsis management and support the transition toward precision-driven critical care.

Keywords: Artificial intelligence; Clinical decision support; Early detection; Precision medicine; Sepsis management.

Fig. 1

Conceptual framework for AI-driven integration of omics data in sepsis care. This schematic illustrates the layered workflow for precision medicine in sepsis, enabled by artificial intelligence (AI). Diverse omics data sources—including routine (genomics, proteomics) and emerging (epigenomics, transcriptomics, metabolomics) modalities—serve as inputs to an AI processing layer. This layer performs tasks such as feature extraction, pattern recognition, biomarker validation, and real-time integration of omics data. The resulting outputs support clinically relevant applications, including subphenotype assignment (e.g., α, β, γ, δ), risk stratification (e.g., 28-day mortality), and personalized treatment guidance (e.g., immunotherapy). This framework highlights the potential of AI to bridge molecular insights and clinical decision-making in sepsis management

Fig. 2

Multi-modal AI architecture for sepsis prediction, clinical support, and therapeutic innovation. This schematic illustrates how diverse input data—ranging from routine clinical variables and raw waveform signals to circulating biomarkers—can be integrated through advanced AI methods to support multiple downstream applications in sepsis care. The AI processing layer employs techniques such as representation learning, biomarker validation, and uncertainty quantification to derive clinically actionable insights. Outputs include early detection systems (e.g., TREWS, InSight), treatment decision support tools (e.g., SERA), and subphenotyping approaches that inform risk stratification, trial enrichment, and immunotherapy timing. Additionally, the framework highlights the emerging role of AI in drug discovery, including the identification of therapeutic targets, candidate repurposing, and personalized prediction of drug-response variability. This figure synthesizes the functional scope of AI across both bedside and translational domains in sepsis management