ReviewCureus2026
Artificial Intelligence (AI) Supported Decision-Making in Intensive Care Units: Implications for Nursing and Medical Practice.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Rethinking preventive medicine education after COVID-19: bridging training gaps in public health practice.Frontiers in 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is transforming intensive care medicine by enabling data-driven, real-time decision-making in complex and high-acuity clinical environments. However, its rapid incorporation into healthcare presents profound ethical, clinical, and professional challenges that warrant comprehensive evaluation. This structured narrative review synthesises literature published between 2015 and 2025 to explore how artificial intelligence supports diagnostic, prognostic, monitoring, and therapeutic decision-making in intensive care units (ICUs) and to assess its implications for nursing and medical practice. The findings reveal that AI enhances diagnostic precision, predictive accuracy, and workflow efficiency while improving patient safety and optimising resource utilisation. Nonetheless, ongoing concerns about interpretability, accountability, data quality, and algorithmic bias underscore the necessity for transparent governance, ethical oversight, and multidisciplinary collaboration. Distinctively, this review integrates technological, ethical, and interprofessional perspectives to present a holistic framework for understanding human-artificial intelligence collaboration in critical care. It emphasises that sustainable adoption depends on explainable, context-sensitive systems, clinician engagement, and the inclusion of AI literacy in professional training. This review advances the discourse by framing AI not as a replacement but as a transformative partner, arguing that the future of intensive care lies in harmonising computational precision with clinical empathy to deliver ethical, equitable, and patient-centred outcomes.
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.