Data-Driven Quality of Care in the ICU: A Concise Review
Authors' conclusion
AI and ML have the potential to transform ICU management. However, their success depends on validated methodologies, interoperable data frameworks, and interpretable models that clinicians can trust.
Study context
- Population
- Artificial intelligence (AI)/machine learning (ML) applications in ICU quality and performance assessment
Methodological detailsconcise review, structured narrative review, PubMed search, English-language articles; original research, reviews, letters and commentaries included
- Concise review
- Structured narrative review
- PubMed search, English-language articles; original research, reviews, letters and commentaries included
Main finding
As reported in the abstract
- AI and ML use clinical data to evaluate ICU performance, measure risk factors, optimise workflows and predict adverse events
- ML-driven models can improve clinical decision-making and ICU management
- Real-world implementation requires rigorous validation and clinician adoption; significant challenges
Original publication
Data-Driven Quality of Care in the ICU: A Concise Review. Crit Care Med. Published 19 September 2025. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →