ReviewMaedica2026
Artificial Intelligence-Enhanced Early Warning Systems for Early Detection of Clinical Deterioration in Hospitalized Patients: Current Clinical Applications and Future Directions.
Review in Maedica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early recognition of clinical deterioration in hospitalized patients is essential for preventing adverse events such as cardiac arrest, sepsis, respiratory failure and unplanned intensive care unit (ICU) admission. Traditional Early Warning Systems (EWS), including the Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), are widely used to support bedside assessment through standardized evaluation of physiological parameters. However, conventional EWS are limited by intermittent monitoring, manual data entry and variable clinical interpretation. Recent advances in electronic health records, continuous physiological monitoring and artificial intelligence (AI) have enabled the development of AI-enhanced Early Warning Systems capable of real-time risk assessment and prediction of clinical deterioration before overt clinical manifestations occur. Machine learning algorithms can analyze large volumes of clinical data, identify subtle physiological trends and support earlier clinical intervention. Emerging evidence suggests that AI-enhanced EWS may improve rapid response activation, reduce ICU transfers, decrease in-hospital mortality and optimize workflow efficiency. This narrative review examines the evolution of EWS from traditional scoring systems to AI-driven predictive models, focusing on their current clinical applications in hospitalized adult patients. Particular emphasis is placed on practical implementation in routine clinical care, the role of nurses and multidisciplinary teams, interpretability of AI algorithms, ethical considerations and barriers to adoption. In addition, the review discusses the integration of AI-enhanced systems into clinical decision support pathways and highlights the importance of maintaining clinical judgment alongside technological innovation. AI-enhanced EWS should be viewed as decision-support tools that complement rather than replace healthcare professionals. Successful clinical implementation requires robust validation, interdisciplinary collaboration, organizational readiness and continuous staff education. When appropriately integrated into clinical workflows, AI-supported predictive systems may significantly contribute to earlier intervention and improved patient safety in acute hospital settings.
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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.