ReviewJournal of intensive medicine2024
Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future.
Review in Journal of intensive medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses 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, 2 syntheses or guidelines pooled it.
- Artificial intelligence in pediatric intensive care units: current applications in sepsis management.World journal of pediatrics : WJP · 2026Pooled it
- Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.BMC infectious diseases · 2025Pooled it
- Earlier ICU Transfer after CONCERN Early Warning System Score Escalation Reduced Sepsis-related Mortality: Results from a Multi-site Pragmatic Cluster Randomized Controlled Trial.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Trial
- From Sentiment to Signal: Narrative-Physiology Discordance as a Testable Target for Artificial Intelligence in Critical Care.Medical sciences (Basel, Switzerland) · 2026Article
- Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care.World journal of critical care medicine · 2026Review
- Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis.Journal of clinical medicine · 2026Review
- Responsible AI for Sepsis Prediction: Bridging the Gap Between Machine Learning Performance and Clinical Trust.Journal of clinical medicine · 2026Article
- The Utility of Automated, Data-Driven Clinical Support in Telemedicine for Critical Illness: A Survey-Based Study.Critical care explorations · 2026Article
- The next frontier in sepsis: connected ICU data for real-world clinical decision making.Intensive care medicine · 2026Review
- Predicting Gram-negative bloodstream infection in elderly patients after isolation of GNB from non-blood specimens: a machine learning-based tool.Frontiers in medicine · 2026Article
- Improving sepsis care with Artificial Intelligence. What would Illich say?Critical care science · 2026Article
- Artificial intelligence based predictive models for early sepsis detection in intensive care units: a scoping review.Frontiers in digital health · 2026Review
- Neonatal and pediatric sepsis: Microbiological insights, diagnostic innovations, and antimicrobial challenges.World journal of clinical pediatrics · 2025Review
- Mitochondrial dysfunction in sepsis-induced liver injury: from pathophysiology to preclinical therapeutic targets.Journal of translational medicine · 2025Review
- Research Progress on Sepsis Diagnosis and Monitoring Based on Omics Technologies: A Review.Diagnostics (Basel, Switzerland) · 2025Review
- Early Sepsis Prediction Using Publicly Available Data: High-Performance AI/ML Models with First-Hour Clinical Information.Diagnostics (Basel, Switzerland) · 2025Article
- Nanomaterial-based encapsulation of biochemicals for targeted sepsis therapy.Materials today. Bio · 2025Review
- Machine Learning-Augmented Triage for Sepsis: Real-Time ICU Mortality Prediction Using SHAP-Explained Meta-Ensemble Models.Biomedicines · 2025Article
- Artificial Intelligence in Sepsis Management: An Overview for Clinicians.Journal of clinical medicine · 2025Review
- Exploring the Potentials of Artificial Intelligence in Sepsis Management in the Intensive Care Unit.Critical care research and practice · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis remains a major challenge internationally for healthcare systems. Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management. In sepsis, mortality increases with every hour left untreated. Artificial intelligence (AI) is transforming worldwide healthcare delivery at present. This review has outlined how AI can augment strategies to address this global disease burden. AI and machine learning (ML) algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods. Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs. This gives providers additional time to plan and execute treatment plans, thereby avoiding increasing complications associated with delayed diagnosis of sepsis. The potential for cost savings with AI implementation is also discussed, including improving workflow efficiencies, reducing administrative costs, and improving healthcare outcomes. Despite these advantages, clinicians have been slow to adopt AI into clinical practice. Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use. Furthermore, the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies. Finally, we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology.
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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.