Mednition Announces Breakthrough AI Sepsis Model with Highest Achieved Performance

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New AI model achieves best-in-class AUC results for ED sepsis diagnosis using Sepsis-3 criteria, demonstrating potential to significantly improve patient outcomes.

Mednition Announces Breakthrough AI Sepsis Model with Highest Achieved Performance

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Mednition today announced the development of a groundbreaking AI sepsis model that has achieved the highest published AUC for the diagnosis of Emergency Department (ED) sepsis using the Sepsis-3 criteria. This breakthrough model, developed on the KATE AI platform, has the potential to revolutionize sepsis detection and treatment, ultimately saving lives.

The AI model achieves an impressive Area Under the Curve (AUC) of 99%, indicating exceptional accuracy in distinguishing between septic and non-septic patients. Notably, the model demonstrated a Sensitivity (TPR) of 95% and a Specificity (TNR) of 96% on a retrospective cohort of 540,884 patients with 14,676 positive sepsis cases across 16 hospital sites. These results, available on mednition.com/research1, confirm the KATE AI platform's ability to effectively identify patients with sepsis using the latest academic definition of Sepsis. This new research builds on the team's prior successes for sepsis screening in Emergency Department Triage. That breakthrough sepsis model, used before any diagnostic labs are available, achieved an AUC of 94%2.

Too often, model performance data is partially disclosed in publications that mask the true performance of AI models. Also, published research has been plagued with low model AUC scores that require a tradeoff between sensitivity and specificity. By publishing the results to fully disclose the model performance in sensitivity, specificity, and model AUC, Mednition intends to redefine the minimum standards necessary for clinical healthcare leadership to transparently evaluate clinical AI.

"We are thrilled to announce the development of this additional groundbreaking sepsis model," said Christian Reilly, co-founder and president at Mednition. "Early and accurate screening and diagnosis of sepsis is critical for improving patient outcomes, and our combined models for sepsis have the potential to transform the way we detect and manage this life-threatening condition. By leveraging the power of AI, we can provide clinicians with the information they need to make faster, more informed decisions, ultimately leading to better patient care."

The KATE AI platform was recently awarded a FDA Breakthrough Device Designation for the sepsis screening model at triage and awarded "Best In Show" for the 2025 HIMSS Global Health Conference & Exhibition Emerge Innovation Experience in the Hospital Systems Toughest Challenges category.

References

1. Ivanov O, Reilly C. Detection of sepsis-3 in the emergency department using machine learning. mednition.com/research. Preprint posted online March 4, 2025.

2. Ivanov O, Molander K, Dunne R, Liu S, Brecher D, Masek K, Lewis E, Wolf L, Travers D, Delaney D, Montgomery K, Reilly C. Detection of sepsis during emergency department triage using machine learning. arXiv. Preprint posted online April 15, 2022. doi:10.48550/arXiv.2204.07657

About Mednition

Mednition was founded in 2014 with a passion for helping clinicians improve healthcare delivery and save lives. Founded with a vision to transform healthcare, Mednition combines the power of EHR-integrated artificial intelligence and clinical expertise to address critical challenges in the healthcare industry. KATE AI, the company's flagship solution, is designed specifically to empower emergency nurses, reduce clinical risk, and improve the quality of care. The company is funded by a select group of private investors and major healthcare financial institutions, including Concord Health Partners (AHA Innovation Development Fund LP), Wildcat Capital Management and Moneta Ventures. The company is based in Burlingame, CA. For more information, visit Mednition.

The AI model achieves an impressive Area Under the Curve (AUC) of 99%, indicating exceptional accuracy in distinguishing between septic and non-septic patients.