AIMC Journal:
Nursing in critical care
Showing 1 to 10 of 12 articles
Development and Validation of an Interpretable Machine Learning Model for Predicting ICU-Acquired Weakness in Postoperative Patients.
Nursing in critical care
Sep 1, 2026
BACKGROUND: ICU-acquired weakness (ICU-AW) is a common and debilitating complication among critically ill patients, particularly those undergoing major surgery. Early identification of patients at high risk of ICU-AW may facilitate timely preventive ...
The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.
Nursing in critical care
Sep 1, 2026
BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in healthcare, with significant potential to enhance nursing practice, particularly in intensive care units (ICUs). ICUs pose complex challenges, ...
Trusting the Algorithm or Trusting the Nurse? Critical Care Nurses' Experiences of Automation Bias and Professional Autonomy in AI-Assisted Early Warning.
Nursing in critical care
Sep 1, 2026
BACKGROUND: Artificial intelligence-assisted early warning systems (AI-EWS) are increasingly integrated into critical care, yet little is known about how nurses experience automation bias and negotiate professional autonomy when algorithmic recommend...
Harnessing Artificial Intelligence to Strengthen Acute and Critical Care Nursing Practice: A Systematic Review.
Nursing in critical care
Sep 1, 2026
BACKGROUND: Artificial intelligence (AI) is reshaping clinical decision support systems (CDSSs). In acute and critical care, nurses provide continuous surveillance, recognise deterioration, coordinate escalation and translate protocols into bedside a...
From Triage to Intensive Care: A Qualitative Study of Nurses' Experiences with AI-Enabled Decision Support.
Nursing in critical care
Jul 1, 2026
BACKGROUND: Artificial intelligence (AI)-enabled decision support systems are increasingly used in emergency departments and intensive care units to support triage, prediction of deterioration, sepsis recognition and escalation decisions. Although th...
Machine Learning Interpretability to Assess the Association Between Time in Tight Range and Mortality in Cardiogenic Shock.
Nursing in critical care
May 1, 2026
BACKGROUND: Cardiogenic shock (CS) is a critical condition of end-organ hypoperfusion with high mortality. Fluctuations in blood glucose (BG) levels may exacerbate cardiovascular instability in critically ill patients. Time In Tight Range (TITR), def...
Assessment of Pain Intensity Using Deep Learning Models in Non-Communicative Intensive Care Patients.
Nursing in critical care
May 1, 2026
BACKGROUND: Pain is a multifaceted and subjective phenomenon frequently experienced by patients in intensive care units. In non-communicating populations, conventional assessment tools are often inadequate and susceptible to observer bias. Deep learn...
Between Algorithm and Instinct: A Phenomenological Study of Critical Care Nurses' Decision-Making in AI-Supported Care.
Nursing in critical care
May 1, 2026
BACKGROUND: Artificial intelligence (AI) is rapidly reshaping critical care through predictive analytics, intelligent monitoring and decision-support tools. While these innovations may enhance early detection and workflow efficiency, they also raise ...
ICU Nurses' Perspectives on Artificial Intelligence in Adult Intensive Care Units: Knowledge, Attitudes and Job-Security Concerns.
Nursing in critical care
May 1, 2026
BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare, particularly in adult intensive care units (ICUs), yet nurses' knowledge, attitudes and concerns regarding AI remain insufficiently examined. This study aimed to ass...
Facilitating the Implementation of Artificial Intelligence as Complex Health Interventions in Intensive Care Nursing.
Nursing in critical care
May 1, 2026
Artificial intelligence (AI) has the potential to integrate and digest vast amounts of information to aid clinical decision-making and organise the logistics, processes and delivery of healthcare services, especially in the areas of patient data anal...
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