Publicação
Data-Driven Quality of Care in the ICU
| Resumo: | OBJECTIVES: Artificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in intensive care medicine. Nevertheless, despite the development of numerous AI/ML models, their integration into routine ICU practice remains limited. This concise review examines the role of AI and data science in critical care, with a focus on their contributions to safety and quality assurance, clinical processes improvements, and ICU management. By synthesizing current evidence, this review aims to highlight the opportunities and challenges associated with implementing AI-driven solutions in critical care settings. DATA SOURCES: English-language articles were identified in PubMed using keywords related to AI, ML, ICU management, clinical decision support, and predictive analytics. STUDY SELECTION: Original research articles, reviews, letters, and commentaries relevant to AI/ML applications in ICU quality and performance assessment were included. DATA EXTRACTION: Relevant literature was identified, key findings were synthesized into a structured narrative review. DATA SYNTHESIS: The integration of AI and ML into ICU management leverages vast clinical data to evaluate ICU performance, measure risk factors, optimize workflows, and predict adverse events. ML-driven models can improve clinical decision-making and ICU management. Despite the promising results, real-world implementation requires rigorous validation and clinician adoption. AI-driven successful implementation in ICU comes with significant challenges. CONCLUSIONS: 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. Advancing AI use in the ICU demands a multidisciplinary effort to create adaptive, transparent, and clinically meaningful solutions that enhance patient care and improve workflow, while ensuring safety and efficiency. |
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| Autores principais: | Moralez, Giulliana M. |
| Outros Autores: | Amado, Filipe; Liu, Vincent X.; Tan, Sing Chee; Meyfroidt, Geert; Stevens, Robert D.; Pilcher, David; Salluh, Jorge I.F. |
| Assunto: | artificial intelligence clinical decision support systems data machine learning quality improvement Critical Care and Intensive Care Medicine |
| Ano: | 2026 |
| País: | Portugal |
| Tipo de documento: | recensão |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | OBJECTIVES: Artificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in intensive care medicine. Nevertheless, despite the development of numerous AI/ML models, their integration into routine ICU practice remains limited. This concise review examines the role of AI and data science in critical care, with a focus on their contributions to safety and quality assurance, clinical processes improvements, and ICU management. By synthesizing current evidence, this review aims to highlight the opportunities and challenges associated with implementing AI-driven solutions in critical care settings. DATA SOURCES: English-language articles were identified in PubMed using keywords related to AI, ML, ICU management, clinical decision support, and predictive analytics. STUDY SELECTION: Original research articles, reviews, letters, and commentaries relevant to AI/ML applications in ICU quality and performance assessment were included. DATA EXTRACTION: Relevant literature was identified, key findings were synthesized into a structured narrative review. DATA SYNTHESIS: The integration of AI and ML into ICU management leverages vast clinical data to evaluate ICU performance, measure risk factors, optimize workflows, and predict adverse events. ML-driven models can improve clinical decision-making and ICU management. Despite the promising results, real-world implementation requires rigorous validation and clinician adoption. AI-driven successful implementation in ICU comes with significant challenges. CONCLUSIONS: 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. Advancing AI use in the ICU demands a multidisciplinary effort to create adaptive, transparent, and clinically meaningful solutions that enhance patient care and improve workflow, while ensuring safety and efficiency. |
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