Harnessing AI for Improved Diagnosis and Management of Pediatric Sepsis: Current Advances, Challenges, and Future Directions

Authors

Pavlos Siolos, 5th Year Pediatrics Resident, First Department of Pediatrics, Aristotle University of Thessaloniki, Hippokration Hospital, Thessaloniki, Greece.
Saif Pasha, 3rd Year Medical Student, Virginia Tech Carilion School of Medicine, Roanoke, VA.
Maria Triantafyllou, 3rd Year Pediatric Resident, Center for Genetic Medicine Research, Children's National Research Institute, Children's National Hospital.
Nora Wolff, 3rd Year Pediatric Resident, Center for Genetic Medicine Research, Children's National Research Institute, Children's National Hospital.
Zara Ibrahim, 3rd Year Pediatric Resident, Center for Genetic Medicine Research, Children's National Research Institute, Children's National Hospital.
Panagiotis Kratimenos, Post-doctoral Fellow, Center for Neuroscience Research, Children's National Research Institute, Children's National Hospital, Washington, DC.
Rishi Kamaleswaran, Associate Professor of Surgery and Anesthesiology, Duke University School of Medicine, Durham, NC.
Tom Velez, CEO at Computer Technology Associates, Cardiff, CA.
Ioannis Koutroulis, 3rd Year Pediatric Resident, Center for Genetic Medicine Research, Children's National Research Institute, Children's National Hospital.

Document Type

Journal Article

Publication Date

7-1-2025

Journal

Pediatric emergency care

Volume

41

Issue

7

DOI

10.1097/PEC.0000000000003397

Keywords

artificial intelligence; early diagnosis; health care technology; machine learning; pediatric sepsis

Abstract

Artificial intelligence (AI) has been applied to early recognition and management of rapidly progressive, community-acquired pediatric sepsis, a leading cause of childhood mortality. The broad adoption of electronic health records combined with rapid advances in digital technologies have enabled the federated training of both knowledge-driven AI, known as expert systems, trained by teams of collaborating clinicians, and data-driven AI, known as machine learning (ML), to derive predictive, clustering algorithms trained on "big data." An important subset of ML is "deep learning," which includes tools that understand, interpret, and manipulate human imagery and language, such as natural language processing and its subset large language models. We are in an era of rapid deployment of AI/ML-powered tools ranging from real-time electronic health records-embedded decision support tools to continuous wearable vital sign monitors and mobile/conversational virtual assistants/triage apps. These applications have the potential of transforming the timeliness of life-saving sepsis care delivery. This review explores the current and potential AI/ML applications in sepsis care, including tools for screening/early detection, risk stratification/outcome prediction, personalized treatment, and continuous patient monitoring. We highlight successful implementations and ongoing clinical trials, emphasizing the impact on patient outcomes. Finally, we address practical considerations for the future, such as bias mitigation and integration into clinical workflows.

APA Citation

Siolos, Pavlos; Pasha, Saif; Triantafyllou, Maria; Wolff, Nora; Ibrahim, Zara; Kratimenos, Panagiotis; Kamaleswaran, Rishi; Velez, Tom; and Koutroulis, Ioannis, "Harnessing AI for Improved Diagnosis and Management of Pediatric Sepsis: Current Advances, Challenges, and Future Directions" (2025). GW Authored Works. Paper 7678.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/7678

Department

Pediatrics