ReviewJournal of intensive medicine2025
Large language models in critical care.
Review in Journal of intensive medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Integrating Large Language Models Into Trauma Education for Medical Students: Randomized Controlled Pilot Trial.JMIR medical education · 2026Trial
- Hallucination Rate of Peer-Reviewed Citations Generated by Large Language Models in Neurocritical Care.Critical care explorations · 2026Article
- Leveraging artificial intelligence to streamline documentation and support patient-centered gynecologic oncology outpatient visits.Gynecologic oncology reports · 2026Article
- The landscape of artificial intelligence-enabled medical devices in the EU and the US intended for intensive care units.NPJ digital medicine · 2026Article
- The regulation of artificial intelligence in intensive care units: from narrow tools to generalist systems.NPJ digital medicine · 2026Review
- Design and Architecture of a Generative-AI-Supported, Nonphysician-Delivered Model for GDMT Optimization in HFrEF: The ASSIST-HF Trial.JACC. Advances · 2026Article
- Artificial intelligence-guided nutritional therapy in the ICU.Current opinion in clinical nutrition and metabolic care · 2026Review
- Artificial Intelligence in Critical Care Nephrology: Current Applications, Emerging Techniques, and Challenges to Clinical Integration.Kidney360 · 2026Review
- From diagnostics to education: Multi-domain evaluation of LLM chatbots in neurology.Journal of Taibah University Medical Sciences · 2026Review
- The "DeepSeek effect" and the adoption-integration gap of generative artificial intelligence in clinical practice: a national online convenience cross-sectional survey of academic critical care physicians in China.Frontiers in medicine · 2026Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- Dataset of anonymized discharge summaries of sepsis patients from a Brazilian tertiary hospital for NLP applications.Data in brief · 2025Article
- Review
- Editorial: Infections in the intensive care unit, volume III.Frontiers in medicine · 2025Article
- Large language models in clinical nutrition: an overview of its applications, capabilities, limitations, and potential future prospects.Frontiers in nutrition · 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
The advent of chat generative pre-trained transformer (ChatGPT) and large language models (LLMs) has revolutionized natural language processing (NLP). These models possess unprecedented capabilities in understanding and generating human-like language. This breakthrough holds significant promise for critical care medicine, where unstructured data and complex clinical information are abundant. Key applications of LLMs in this field include administrative support through automated documentation and patient chart summarization; clinical decision support by assisting in diagnostics and treatment planning; personalized communication to enhance patient and family understanding; and improving data quality by extracting insights from unstructured clinical notes. Despite these opportunities, challenges such as the risk of generating inaccurate or biased information "hallucinations", ethical considerations, and the need for clinician artificial intelligence (AI) literacy must be addressed. Integrating LLMs with traditional machine learning models - an approach known as Hybrid AI - combines the strengths of both technologies while mitigating their limitations. Careful implementation, regulatory compliance, and ongoing validation are essential to ensure that LLMs enhance patient care rather than hinder it. LLMs have the potential to transform critical care practices, but integrating them requires caution. Responsible use and thorough clinician training are crucial to fully realize their benefits.
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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.