ReviewJournal of clinical medicine2025
Machine Learning and Artificial Intelligence in Intensive Care Medicine: Critical Recalibrations from Rule-Based Systems to Frontier Models.
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 19 papers.
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
19 citing papers in PubMed.
- Artificial intelligence assisted telemedicine, clinical decision support for anesthesia and critical care in intensive care units: a scoping review.BMC anesthesiology · 2026Article
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Phenotype discovery and mortality prediction in sepsis-induced myocardial dysfunction: a deep learning and stratified modeling approach.BMC medical informatics and decision making · 2026Article
- Artificial intelligence-guided tools in adult ECMO: Current advancements, emerging Trends and future directions.Perfusion · 2026Review
- Adaptive AI framework for pharmacokinetics using GATs, transformers, and AutoML.Molecular diversity · 2026Article
- Cross-algorithm machine learning and consensus features for early disseminated intravascular coagulation risk prediction in sepsis-induced coagulopathy.Journal of translational medicine · 2026Article
- Clinical prediction of the mortality for acute kidney injury in decompensated cirrhosis.Scientific reports · 2026Article
- Artificial Intelligence in Parenteral Nutrition: Enhancing Patient Outcomes Through Global Experience and the Bulgarian Context.Nutrients · 2026Review
- Beyond One-Size-Fits-All: Precision Mechanical Ventilation in ARDS.Journal of clinical medicine · 2026Review
- Review
- Ethical Responsibility in Medical AI: A Semi-Systematic Thematic Review and Multilevel Governance Model.Healthcare (Basel, Switzerland) · 2026Review
- Navigating the artificial intelligence landscape in trauma, critical care and emergency general surgery: insights from the American Association for the Surgery of Trauma (AAST) 2025 Annual Meeting Panel Discussion.Trauma surgery & acute care open · 2026Review
- Ethical oversight of AI-driven paediatric trials: a proactive, risk-sensitive interim review model.Frontiers in digital health · 2026Article
- Artificial Intelligence in Intensive Care: An Overview of Systematic Reviews with Clinical Maturity and Readiness Mapping.Journal of clinical medicine · 2025Review
- Digital Transformation in Critical Care: Implications for Quality of Care, Infection Control, and Clinical Outcomes.Journal of clinical medicine · 2025Article
- Fluid Creep as an Independent Predictor of Fluid Overload and Mortality in Critically Ill Patients: A Cohort Study.Life (Basel, Switzerland) · 2025Article
- Multi-omics profiling and AI-driven clinically deployable risk models in MGUS and smoldering myeloma.Clinical and experimental medicine · 2025Review
- Interpretable Adaptive Graph Fusion Network for Mortality and Complication Prediction in ICUs.Diagnostics (Basel, Switzerland) · 2025Article
- Healthcare 5.0-Driven Clinical Intelligence: The Learn-Predict-Monitor-Detect-Correct Framework for Systematic Artificial Intelligence Integration in Critical Care.Healthcare (Basel, Switzerland) · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and machine learning (ML) are rapidly transforming clinical decision support systems (CDSSs) in intensive care units (ICUs), where vast amounts of real-time data present both an opportunity and a challenge for timely clinical decision-making. Here, we trace the evolution of machine intelligence in critical care. This technology has been applied across key ICU domains such as early warning systems, sepsis management, mechanical ventilation, and diagnostic support. We highlight a transition from rule-based systems to more sophisticated machine learning approaches, including emerging frontier models. While these tools demonstrate strong potential to improve predictive performance and workflow efficiency, their implementation remains constrained by concerns around transparency, workflow integration, bias, and regulatory challenges. Ensuring the safe, effective, and ethical use of AI in intensive care will depend on validated, human-centered systems supported by transdisciplinary collaboration, technological literacy, prospective evaluation, and continuous monitoring.
Indexed as
Identifiers
What Socratic 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 Socratic graph.