ReviewJournal of clinical medicine2025
Artificial Intelligence in Sepsis Management: An Overview for Clinicians.
Review in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.BMC infectious diseases · 2025Pooled it
- Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care.World journal of critical care medicine · 2026Review
- Systems Bioengineering of Septic Shock Metabolism: Citrulline, β-Hydroxybutyrate and Plasma Biomarker-Based Phenotyping.Biomolecules · 2026Review
- Current adoption of the Prosthodontic Diagnostic Index for clinical care-A cross-sectional survey study.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2026Article
- Artificial intelligence in the emergency department-- applications, perceptions and limitations: a narrative review.World journal of emergency medicine · 2026Review
- Real-Time Artificial Intelligence for Early Sepsis Prediction Using Dynamic Clinical Data: A Systematic Review.Cureus · 2026Review
- The Sepsis ImmunoScore Predicts Sepsis, Mortality, and Deterioration Better than Clinical Scores and Widely Available Biomarkers.Diagnostics (Basel, Switzerland) · 2026Article
- Dissecting the pathobiology of suspected sepsis through a comparative analysis of endothelial inflammatory and clinical prediction models.Scientific reports · 2026Article
- Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis.Journal of clinical medicine · 2026Review
- Article
- Challenges in early detection and prognostication of sepsis: new approaches from the emergency department and intensive care unit.EClinicalMedicine · 2026Review
- Predictive Model of Dynamic Subphenotypes for 30-Day Mortality in Emergency Department Patients with Suspected Infection Using the Vital Signs of the First 24 Hours: An Analytical Cohort Study in a Tertiary Care Clinic.Journal of clinical medicine · 2026Article
- Responsible AI for Sepsis Prediction: Bridging the Gap Between Machine Learning Performance and Clinical Trust.Journal of clinical medicine · 2026Article
- Conformance-Aware Predictive Process Monitoring for Early Detection of Sepsis Deterioration Using Incomplete Care Pathways.Journal of clinical medicine · 2026Article
- The role of the HOTEL score in predicting prognosis in patients with sepsis and septic shock in the emergency department.Internal and emergency medicine · 2026Observational
- Hospital-Wide Sepsis Detection: A Machine Learning Model Based on Prospectively Expert-Validated Cohort.Journal of clinical medicine · 2026Article
- Hyper-inflammation and immunosuppression: redefining sepsis therapy using modern approaches.Frontiers in pharmacology · 2026Review
- Translational Potential and Explainability of Artificial Intelligence-Based Clinical Decision Support for Adults in Intensive Care: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
- Crosstalk between innate immune signaling pathways and integrated TLR, NLRP3 inflammasome, cGAS-STING, and NF-κB networks in sepsis.Frontiers in cell and developmental biology · 2026Review
- The Infectious Diseases Orchestrator: Embracing AI Literacy in the Agentic Era.Open forum infectious diseases · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is one of the leading causes of mortality in hospital settings, and early diagnosis is a crucial challenge to improve clinical outcomes. Artificial intelligence (AI) is emerging as a valuable resource to address this challenge, with numerous investigations exploring its application to predict and diagnose sepsis early, as well as personalizing its treatment. Machine learning (ML) models are able to use clinical data collected from hospital Electronic Health Records or continuous monitoring to predict patients at risk of sepsis hours before the onset of symptoms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.