How AI Supports Early Sepsis Detection in ICU and NICU Patients

Sepsis is a life-threatening condition where an infection triggers the body’s immune system to cause widespread organ dysfunction. Early recognition remains one of the greatest challenges in critical care since patients can deteriorate rapidly, and organ damage may already have begun by the time obvious symptoms appear. Hospitals are increasingly evaluating artificial intelligence (AI)-based tools to improve early sepsis detection. Some are integrated into bedside monitors and neonatal incubators. While early studies have reported promising results, findings have been inconsistent. Further research is essential before these tools can be widely adopted in clinical practice.

Challenges in Early Sepsis Detection

Sepsis remains one of the leading causes of mortality worldwide, accounting for about 11 million deaths annually, with many cases occurring in intensive care units (ICUs).¹ Early recognition is critical because patients can deteriorate rapidly. However, initial physiological changes, such as increases in heart rate or decreases in blood pressure, may be difficult to distinguish from those of other clinical conditions.

Commonly used clinical scoring systems, including the Systemic Inflammatory Response Syndrome (SIRS) criteria, the Sequential Organ Failure Assessment (SOFA), and the quick SOFA (qSOFA), were developed primarily to identify patients with established or worsening sepsis rather than predict the condition before clear clinical signs appear. Studies have shown that these tools may have limited sensitivity for early sepsis detection or may generate excessive false-positive alerts.¹

Early detection is particularly challenging in neonatal intensive care units (NICUs), since premature infants cannot communicate symptoms and physiologic parameters normally fluctuate with development. As a result, clinicians rely heavily on continuous incubator-based monitoring, including heart rate, oxygen saturation, and skin-to-air temperature differences, to identify early signs of sepsis.²

AI Shows Promise for Early Sepsis Detection

AI is increasingly being investigated as a tool to improve early sepsis detection by analyzing multiple physiological signals simultaneously. By evaluating changes across multiple parameters over time, AI models may detect subtle signals before clinical signs appear.

In NICUs, researchers have developed machine learning models that combine continuously monitored data, including heart rate and oxygen saturation, to identify infants at increased risk of sepsis before clinical signs appear. These models are well-suited for the NICU since premature infants are already continuously monitored. Incubators and bedside monitors continuously collect vital sign data, enabling AI to detect subtle changes that may signal sepsis.²

Evidence in adult ICUs is growing but remains more heterogeneous. A 2026 review of 37 studies reported that several AI models achieved area under the receiver operating characteristic curve (AUROC) values exceeding 0.90 and identified patients at risk for sepsis 3 to 24 hours before clinical diagnosis.¹ In another study, researchers developed an AI model using a large public ICU database. The model achieved 92% accuracy in predicting sepsis, respiratory failure, and cardiovascular complications up to 12 hours before onset. It also reduced false-positive alerts by 35% compared with the National Early Warning Score (NEWS).³

Several AI-based sepsis detection systems have also progressed into clinical practice. Duke Health's Sepsis Watch has been integrated into emergency department and ICU workflows since 2018. Research has shown that the use of the watch is associated with a 27% lower sepsis-related mortality.³ In a 2025 study, researchers evaluated the model at a separate health system in Ohio and found that it maintained strong performance across 4 emergency departments, with AUROC values ranging from 0.906 to 0.960.⁴

In April 2024, the FDA authorized the Prenosis Sepsis ImmunoScore, the first AI-enabled diagnostic support tool cleared to predict sepsis risk within 24 hours of ICU admission.³ Research also suggests that incorporating continuously monitored physiological variables, such as blood pressure, temperature, and oxygen saturation, can improve sepsis prediction compared with symptom-based models alone.⁵

As AI systems become more widely adopted, model interpretability remains an important concern. To improve clinician confidence and support decision-making, some researchers have added explainable AI tools such as Shapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). These tools help to identify the clinical variables that contribute most to an individual's prediction.⁶

Current Limitations To Clinical Adoption

Despite several advances, certain important limitations with AI usage remain. Many AI models may identify physiological changes that occur after sepsis has already developed rather than predict the condition before its onset. Another challenge is limited external validation. Most models have been developed and tested retrospectively using patient data from a single institution rather than being evaluated across multiple health care settings. A systematic review and meta-analysis of AI-based sepsis detection in hospitalized adults found that although many models performed well during development, evidence supporting their real-world clinical performance remains limited.¹

Additionally, most of the published research has been conducted in hospitals in the US and China, raising concerns about how well these models would generalize to other patient populations and health care systems.¹

Future Directions for AI-Based Sepsis Detection

Future research will need to address several key challenges before AI-based sepsis detection tools can be more widely adopted. Future studies should test these tools in real time and across different hospitals, rather than relying only on past patient records. They should also report false-positive rates and show whether AI can truly identify sepsis before symptoms become clinically relevant.

For infection prevention and critical care teams, accuracy alone is not enough. The strongest evidence comes from AI tools evaluated in real clinical settings and linked to patient outcomes. Studies of Duke Health's Sepsis Watch and the FDA-cleared Prenosis Sepsis ImmunoScore represent important steps in that direction. As AI continues to evolve, demonstrating real-world clinical benefit will be key to broader adoption.

References

1. González Garcés M, Valencia García J, Jiménez Triana C, Cárdenas Montoya J, Hernández Rincón EH. Artificial intelligence-based predictive models for early sepsis detection in intensive care units: a scoping review. Front Digit Health. 2026;8:1794922. doi:10.3389/fdgth.2026.1794922
2. Sullivan BA, Kausch SL, Fairchild KD. Artificial and human intelligence for early identification of neonatal sepsis. Pediatr Res. 2023;93(2):350-356. doi:10.1038/s41390-022-02274-7
3. Siddiqui MM. Advanced AI-based early warning framework for ICU patient monitoring using temporal clinical data. Biomedical and Pharmacology Journal. 2026;19(1):70-78. doi:10.13005/bpj/3338
4. Valan B, Prakash A, Ratliff W, et al. Evaluating sepsis watch generalizability through multisite external validation of a sepsis machine learning model. npj Digit Med. 2025;8(1):350. doi:10.1038/s41746-025-01664-5
5. Aityan S, Herrero R, Mosaddegh A, et al. AI-powered early detection of sepsis in emergency medicine. Life. 2025;15(10):1576. doi:10.3390/life15101576
6. Muskan P, Khan R, Banu M, Shiragumpi P, Meena K. AI-Powered Early Sepsis Detection System for ICU with Clinical Explainability. International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 9, pp.c945-c950, September 2025.