Hospital-Based Medicine

Expert Evaluation and Consensus on GPT-4o Summaries of Clinical Letters: Validation and Results of the Framework and Implementation of AI Tools Project.

BACKGROUND: Large language models (LLMs) are increasingly used to summarize clinical documents; yet, automated metrics often inadequately capture clinical relevance and safety. In the initial phase of the "Framework and Implementation of AI Tools," an expert-driven, cocreated evaluation methodology was established to assess LLM-generated discharge letter summaries, combining prompt design consider...

Towards naturalistic social neuroscience: A multi-level framework integrating real-world phenotyping, neurobiology, and computational mechanisms.

Humans are inherently social species. Our behavior is heavily influenced by the social environment and context. While the traditional social neuroscience approach has made significant progress in mapping isolated social cognitive processes, it often fails to capture the complexity of real-world social environment-where perception, decision-making, and interaction unfold simultaneously and contextu...

Design and methodology of the AI-empowered Clinical Evidence for Integrated Chinese-Western Medicine (ACE-iMed) platform.

BACKGROUND: Integrated Chinese-Western medicine (ICWM) is a distinctive medical system that plays an important role in healthcare and has received inc...

AI-enhanced system dynamics simulation for sustainable urban development under water constraints: A case study of Langfang, China.

Water-scarce cities undergoing rapid urbanization must sustain economic growth while complying with stringent water-resource and environmental constra...

Predicting length of stay in the pediatric intensive care unit at a tertiary center in Saudi Arabia using machine learning.

BACKGROUND: Prolonged stay in pediatric intensive care units (PICUs) is associated with increased mortality risk, elevated healthcare costs, and dimin...

Applications of artificial intelligence in nuclear medicine.

Artificial intelligence (AI) has vast potential to reshape nuclear medicine. Applications can be found at every step of the processing workflow, inclu...

Artificial intelligence model predicts malignant transformation of oral leukoplakia and optimizes interventions.

PURPOSE: Oral leukoplakia (OL), the most common oral potentially malignant disorder (OPMD), poses a significant risk for transformation to oral squamo...

The association between plasma IgG N-glycosylation and viral encephalitis in children: a hospital-based case-control study.

BACKGROUND: Viral encephalitis (VE) is an acute inflammatory disease caused by viral infection. Children are at a significantly higher risk of develop...

Superlearner can predict in-hospital mortality risk in critically ill patients with ischemic stroke: development and international validation.

BACKGROUND: Critically ill patients with ischemic stroke face substantial in-hospital mortality. Early and accurate prediction of mortality risk may f...

Establishing a Sustainable Artificial Intelligence Infrastructure for Health Care Systems Part II: Consideration of Key Regulatory and Bioethical Barriers to Implementation.

Establishing an artificial intelligence (AI) infrastructure is contingent upon processes being conducted within structured regulatory frameworks that ...

The Actionable Innovation Day Approach: Participatory Model for Advancing Critical Care Innovation.

Health care innovation is essential for improving patient outcomes, enhancing system efficiency, and preparing for future challenges; however, meaning...

Age-related differences in surgical outcomes for traumatic central cord syndrome: a multi-institutional causal machine learning analysis.

OBJECTIVE: Traumatic central cord syndrome (TCCS) is the most common incomplete spinal cord injury, yet the optimal management strategy remains contro...

Forecasting hospital bed occupancy: a time series approach with prophet.

BACKGROUND: Accurate hospital bed occupancy forecasting is essential for effective resource planning and patient flow management. While complex machin...

Validation of a machine learning model for predicting early deterioration in the emergency department.

Early recognition of patients at risk for deterioration in the emergency department (ED) is critical for patient safety. Traditional early warning sco...

Identifying best practices for RTTs and dosimetrists: a Delphi consensus study on standardizing workflows in proton therapy.

BACKGROUND AND PURPOSE: A recent scoping review identified gaps in proton therapy (PT) guidelines, especially in fields of expertise of radiation ther...

Digital emergency routing: analysis of feasibility, utilization, and equity implications.

BACKGROUND: Digital emergency care applications offer potential to reduce delays, enhance triage, and improve care coordination, yet evidence remains ...

AI in practice: trends in hospital and clinic libraries.

The rise of artificial intelligence (AI) has introduced new possibilities for hospital and clinic libraries. A research project surveyed hospital and ...

Machine Learning-Based Prediction Model for Infectious Complications in Trauma and Its Association With In-Hospital Mortality.

BACKGROUND: Infectious complications, such as sepsis or catheter-related infections, are common and serious sequelae after trauma. Despite their clini...

Imaging and AI in tertiary prevention of lung cancer: Narrative review and clinical perspectives.

OBJECTIVE: To provide a structured narrative review of current evidence and future directions for artificial intelligence (AI) applications in the ter...

The MR quality landscape in Europe.

OBJECTIVES: Over the last few years, with the introduction of advanced MR imaging techniques, increasing exam demand and the growth of multi-center cl...