Latest AI and machine learning research in patient safety / risk management for healthcare professionals.
Entropy-based analysis is increasingly used in task-based functional magnetic resonance imaging (fMRI) to quantify neural signal complexity and information dynamics, but variation in entropy definitions, parameter choices, and analytic scope can limit cross-study comparability. To systematically review how entropy measures are implemented, parameterized, and interpreted in task-based fMRI studies ...
BACKGROUND: Abdominal aortic aneurysm (AAA) rupture remains a major cause of mortality, and diameter-based surveillance is an imperfect predictor of risk. Some aneurysms rupture below operative thresholds, whereas others remain stable despite exceeding them. Machine learning (ML) may improve risk stratification by integrating geometric, hemodynamic, radiomic, and clinical data. We performed a syst...
BACKGROUND: Fuzzy logic has been progressively investigated as a viable alternative to traditional statistical and machine learning methods in health ...
Academic research is not always available in a form that is accessible or engaging to a non-academic audience, hindering readers' engagement with it. ...
Clinical decision support (CDS) software plays an increasingly central role in health-care delivery, yet the ambiguous interpretations of regulations ...
AIM: The aim of this study is to assess nurse practitioner students' perceptions and engagement with Isabel's artificial intelligence (AI) based diffe...
BACKGROUND: Generative artificial intelligence (AI) models such as ChatGPT have demonstrated potential in medical education and patient communication ...
BACKGROUND: Academic publishing underpins surgical decision-making, but the rapid adoption of generative artificial intelligence (AI) raises concerns ...
BACKGROUND AND OBJECTIVES: Family caregivers face elevated risks for mental and physical health issues but have difficulty accessing informational and...
BACKGROUND: Ethics review of research and innovation activities face growing challenges due to rapid scientific and technological advances, interdisci...
AIM: To describe contemporary peer-reviewed literature on artificial intelligence in nurses' clinical decision-making. METHODS: An umbrella review of ...
OBJECTIVES: To assess how disclosing artificial intelligence (AI) results, particularly discordant findings, affects patient trust, anxiety, follow-up...
Integrating artificial intelligence (AI) into maternal and neonatal health (MNH) offers significant opportunities for enhancing patient care through a...
INTRODUCTION: Early chronic obstructive pulmonary disease (COPD) is considered to represent the initial phase of the disease. However, inconsistent te...
PURPOSE: Standard supervised learning assumes deterministic labels (e.g., positive or negative, present or absent), neglecting the diagnostic uncertai...
PURPOSE: To assess narrative clinical documentation quality in assisted reproductive technology (ART) consultations, by applying a transparent, reprod...
OBJECTIVE: To develop educational artificial intelligence (AI)-generated videos for patients undergoing strabismus surgery and assess patient percepti...
The digitization of research has transformed how evidence is gathered, hypotheses are generated, and manuscripts are written, introducing ethical chal...
This Viewpoint critically evaluates the impact of commodifying health data in the era of electronic health records and the ethical challenges this pra...