ReviewCritical care (London, England)2025
Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22.
Review in Critical care (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic Review of Artificial Intelligence Applications in Clinical Trials for Central Nervous System Injuries.Current neuropharmacology · 2026Pooled it
- Global trends and thematic clusters in healthcare professional education, medical errors, and patient safety: a bibliometric analysis, 2000-2026.Frontiers in medicine · 2026Pooled it
- STREAM: A data-driven framework for physiological state monitoring in ICU patients.PLOS digital health · 2026Article
- Fully flexing the potential of AI in intensive care: reinforcement learning with randomization when needed.Intensive care medicine · 2026Article
- Physiology-guided mechanical ventilation: Monitoring, proportional assist, and bounded automation.World journal of critical care medicine · 2026Review
- From Intelligent Operating Room to Smart ICU: Digital Continuity, AI-Supported Decision-Making and CRRT as a Model of Pharmacokinetic Personalization in Critical Care.Healthcare (Basel, Switzerland) · 2026Review
- A pragmatic risk-stratified framework for using large language models in intensive care medicine: A narrative review.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2026Review
- Novelty to necessity: large language models in clinical practice-a call to action for physicians.Internal and emergency medicine · 2026Review
- From frailty-driven to frailty-informed care in the age of wearable AI.Communications medicine · 2026Review
- Smart Cardiac ICU: Digital Integration, Predictive Analytics, and Perioperative Inflammation.Bioengineering (Basel, Switzerland) · 2026Review
- Technology integration in intensive care medicine through clinical engineering and convergent biotechnology.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2026Article
- Explainable Artificial Intelligence in Critical Care Nursing: A Discussion Paper.Nursing in critical care · 2026Article
- Vitabel: A Python Framework for Visualizing and Labelling High-Resolution Physiological Data for Critical Care Machine Learning.Journal of medical systems · 2026Article
- From Intensive Care Unit Prediction to Intensive Care Unit Accountability: A Global Readiness Passport for Machine-learning Prognostication.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- Artificial intelligence-guided tools in adult ECMO: Current advancements, emerging Trends and future directions.Perfusion · 2026Review
- Quality Assessment of the Iran Intensive Care Unit Registry: A Validation Evaluation Using International Medical Registry Frameworks (2017-2024).Iranian journal of medical sciences · 2026Article
- Mechanical ventilation in ARDS: navigating the fine line with real-time monitoring.Critical care (London, England) · 2026Review
- Artificial intelligence in emergency medicine critical care.Clinical and experimental emergency medicine · 2026Article
- Real World Human-LLM Interactions - Prospective blinded versus unblinded expert physician assessments of LLM responses to complex medical dilemmas.PLOS digital health · 2026Article
- Strategies for a Rational Use of Opioids in Critical Care Settings.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) is rapidly transforming the landscape of critical care, offering opportunities for enhanced diagnostic precision and personalized patient management. However, its integration into ICU clinical practice presents significant challenges related to equity, transparency, and the patient-clinician relationship. To address these concerns, a multidisciplinary team of experts was established to assess the current state and future trajectory of AI in critical care. This consensus identified key challenges and proposed actionable recommendations to guide AI implementation in this high-stakes field. Here we present a call to action for the critical care community, to bridge the gap between AI advancements and the need for humanized, patient-centred care. Our goal is to ensure a smooth transition to personalized medicine while, (1) maintaining equitable and unbiased decision-making, (2) fostering the development of a collaborative research network across ICUs, emergency departments, and operating rooms to promote data sharing and harmonization, and (3) addressing the necessary educational and regulatory shifts required for responsible AI deployment. AI integration into critical care demands coordinated efforts among clinicians, patients, industry leaders, and regulators to ensure patient safety and maximize societal benefit. The recommendations outlined here provide a foundation for the ethical and effective implementation of AI in critical care medicine.
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.