Hospital-Based Medicine

Artificial Intelligence in Surgery Revisited: Leveraging AI for Postoperative Instructions, Clinical Support, and Intelligent Monitoring.

Artificial intelligence (AI) is rapidly transforming surgical care, with growing integration across all phases from preoperative planning to postoperative recovery. The role of AI in postoperative care represents a particularly promising frontier. Applications such as AI-generated discharge instructions, conversational chatbots, and computer vision-based wound monitoring have the potential to impr...

Development of an Explainable Machine Learning Model to Predict Mortality Risk in Sepsis Patients: Insights From a Real-World Clinical Data.

BACKGROUND: Sepsis is a life-threatening dysregulated host response to infection; early risk stratification is essential to guide intensive care. OBJECTIVE: To develop and interpret a machine learning model for predicting in-hospital mortality among intensive care unit (ICU) patients with sepsis. METHODS: We retrospectively analyzed clinical data from sepsis patients admitted to the ICU of the Fir...

Artificial intelligence in early onset scoliosis: a scoping review.

PURPOSE: Early onset scoliosis comprises spinal deformities in children younger than 10, creating challenges in diagnosis, risk assessment, and manage...

A Window Into Vulnerability and Support: At-Risk Mothers' Perinatal Experiences and Perspectives on mHealth-Based Care.

AIM: To examine the perinatal experiences of at-risk mothers and their engagement with mobile-health-based care. DESIGN: A qualitative descriptive stu...

Nurse Leadership and Artificial Intelligence Integration in Nursing Workforce Management: A Scoping Review.

AIM: To systematically map evidence on the application of AI systems in nursing workforce management, with a targeted focus on the role of nurse leade...

Patients' and physiotherapists' experiences with robotic technologies for lower extremity rehabilitation following spinal cord injury: a reflexive thematic analysis.

PURPOSE: To explore perceived benefits, barriers and motivational factors related to exercising with the robotic device ROBERT® among patients undergo...

GPT-4o in Nutrition for Inpatients Undergoing Post-Stroke Rehabilitation: Identifying Dietary Errors, Exploring Expert-AI Rationale Differences, and Structuring AI-Expert Collaboration.

OBJECTIVE: Inpatients undergoing stroke rehabilitation experience high malnutrition rates, requiring strict dietary management. However, manual and ti...

Validity and transferability of Model for ASsessing the value of Artificial Intelligence (MAS-AI).

OBJECTIVES: In 2022, a multidisciplinary group of experts and patients published a Model for ASsessing the value of AI (MAS-AI) in medical imaging. MA...

From objective grouping to fuzzy reference intervals: A standardized machine learning approach for thyroid function tests.

BACKGROUND: Accurate interpretation of thyroid function tests (TFTs) requires reliable reference intervals (RIs). Indirect methods based on retrospect...

Characterizing and predicting refractory rumination in obsessive compulsive disorder.

Rumination is problematic for individuals with obsessive compulsive disorder (OCD), and yet, is not addressed in standard treatment for OCD. Further, ...

Therapeutic Drug Monitoring and Point-of-Care Technologies: Opportunities and Current Challenges.

BACKGROUND: This review re-evaluates therapeutic drug monitoring (TDM) by comparing the current analytical and subsequent clinical interpretation capa...

The Impact of Comorbidity Patterns on Clinical Outcomes in Heart Failure: A Machine Learning-Based Cluster Analysis.

Heart failure (HF) is a major global health burden, and complex comorbidity patterns can worsen clinical outcomes and complicate patient care. This st...

The Mediation of AI Trust on AI Uncertainties and AI Competence Among Nurses: A Cross-Sectional Study.

AIM: This study aimed to validate the mediating role of nurses' AI trust in the relationship between AI uncertainties and AI competence. DESIGN: A cro...

Development of an interpretable machine learning model to predict short-term bleeding risk in patients receiving dual antithrombotic therapy following cardiac surgery.

INTRODUCTION: Bleeding is a serious complication in cardiac surgery, especially among patients receiving combined anticoagulant and antiplatelet thera...

Knowledge and Opinions of Operating Room Nurses About Artificial Intelligence: A Descriptive Cross-Sectional Study.

AIMS AND OBJECTIVES: To assess the knowledge and opinions of operating room nurses about artificial intelligence. BACKGROUND: Artificial intelligence ...

Nivolumab plus ipilimumab for potentially resectable hepatocellular carcinoma: Long-term efficacy and biomarker exploration.

BACKGROUND & AIMS: Immune checkpoint inhibitor-based combination therapy has demonstrated high objective response rates in patients with hepatocellula...

Comparative Evaluation of ChatGPT-4o and Grok-3 on Cleft Lip and Palate and Presurgical Infant Orthopedics: A Multidisciplinary Assessment by Orthodontists, Pediatricians, and Plastic Surgeons.

Objective: This study aimed to evaluate and compare the accuracy, clarity, and clinical applicability of 2 state-of-the-art large language models (LLM...

Smart Hospital Infrastructure: Enhancing Parking Management and Ambulance Accessibility in Urban Healthcare Systems.

Hospitals face significant challenges in parking management and assessing ambulances due to fast-growing urbanization, high population density, and tr...

Immune checkpoint molecules as predictive markers of COVID-19 severity: A comprehensive univariable and multivariable analysis.

Immune dysregulation plays a key role in the deterioration of COVID-19. This study evaluated immune checkpoint molecules (ICMs) as markers of disease ...

Toward Standardized ICU Trauma Resuscitation via Markov Decision Processes.

OBJECTIVE: Guideline-based recommendations for posthemostasis resuscitation in trauma patients remain limited. This study aimed to define an interpret...