Hospital-Based Medicine

Systematic assessment of the medical utility of radiology and diagnostic Artificial Intelligence in fracture detection (SAMURAI-fracture): a protocol for a multicentre cluster-randomised controlled trial.

INTRODUCTION: Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient outcomes, unnecessary treatments and significant healthcare costs. Artificial intelligence (AI)-assisted fracture detection tools are now available for use in radiology workflows; however, the impact of these technologies on patient outcomes, experi...

Multidisciplinary dental treatment planning by artificial intelligence: A comparative evaluation.

PURPOSE: The purpose of this study was to evaluate and compare four AI software programs-ChatGPT-5, Microsoft Copilot, Google Gemini (V 2.5), and OpenEvidence-in generating comprehensive dental treatment plans for minimally destructed and severely mutilated teeth using identical clinical inputs. MATERIAL AND METHODS: Ten anonymized clinical cases, each consisting of 1 intraoral photograph and 1 co...

AI and neuroimaging in autism spectrum disorder: advances in diagnosis, methodological challenges and future directions.

BACKGROUND: Autism Spectrum Disorder (ASD) is characterized by challenges in social interaction, communication, and restricted or repetitive behaviors...

AI-assisted radiographic fracture detection and length of stay in the adult ambulatory orthopedic emergency department: a before-after cohort study with a disease-specific internal control.

BACKGROUND: Although artificial intelligence-assisted radiographic fracture detection tools (AI-RFDT) have demonstrated high diagnostic accuracy in ad...

Discordance Between Textual Reasoning and Visual Interpretation in Large Language Models for Low Back Pain: Cross-Sectional Quantitative Evaluation and Exploratory Multimodal Stress Test.

BACKGROUND: Large language models (LLMs) are rapidly evolving from text-based agents to multimodal systems capable of interpreting medical images. Whi...

A Systematic Evaluation of Cohort Selection Criteria and Their Impact on Machine Learning Model Performance and Demographic Disparities in COVID-19 Outcomes: Cohort Study.

BACKGROUND: Cohort selection criteria play a critical role in shaping machine learning (ML) model performance and the equity of clinical outcome predi...

Artificial intelligence in pituitary medicine: what should an endocrinologist know?

Pituitary disorders often include complex radiology imaging, rare clinical presentations, and need for multidisciplinary decision-making and prolonged...

Artificial intelligence for predicting perioperative anaesthetic complications and supporting clinical decision-making: a scoping review.

Perioperative anaesthetic complications are a major public health concern, as they are associated with increased mortality, prolonged hospital stay, h...

3D interactive radiology reports: referring physicians' acceptance of artificial intelligence varies across practice settings.

INTRODUCTION: A 3D interactive report is a state-of-the-art artificial intelligence (AI) tool that integrates a patient's imaging history into an intu...

Clinical Laboratory Terminology Standardization for Semantic Interoperability Using a Large Language Model-Based Agent: Methodological Study.

BACKGROUND: Semantic interoperability, the ability of disparate health information systems to exchange and consistently interpret clinical data, is a ...

Voice-interactive virtual simulated patients as a complement to standardized patient training in pharmacy education: A pilot evaluation.

INTRODUCTION: Standardized patients (SPs) are widely used in pharmacy education for communication skills training, but high resource demands limit pra...

A grade-specific clinical prediction rule for independent walking after traumatic spinal cord injury.

OBJECTIVE: To identify significant predictors for individual American Spinal Injury Association Impairment Scale (AIS) grades, and develop a clinical ...

Comparative performance of artificial intelligence chatbots in patient education for robot-assisted radical prostatectomy: quality, transparency and readability.

Patients undergoing robot-assisted radical prostatectomy require complex counseling regarding treatment selection, cancer control, urinary continence,...

Integrated evaluation of rapid diagnostic testing, genotypic-phenotypic resistance profiling, and AI-Assisted prediction models for antimicrobial stewardship and clinical outcomes in a resource-limited setting.

INTRODUCTION: Antimicrobial resistance (AMR) remains a major global health concern, necessitating timely and accurate diagnostic approaches to guide a...

The Value Opportunity from Artificial Intelligence in U.S. Health Care Spending.

Artificial intelligence (AI) offers the potential to improve productivity and reduce waste across the U.S. health care system. Quantifying achievable ...

Global trends and emerging frontiers in robot-assisted vascular intervention: a comprehensive bibliometric analysis.

Robot-assisted vascular intervention may reduce occupational radiation exposure, improve procedural stability, and expand access to specialized endova...

AI-Assisted Line-Field Confocal Optical Coherence Tomography to Detect Subclinical Basal Cell Carcinoma.

IMPORTANCE: Basal cell carcinoma (BCC) is the most common type of skin cancer worldwide. Particularly on the face, its locally destructive growth lead...

Machine Learning-Based Risk Prediction of In-Hospital Mortality in Patients With Non-ST-Elevation Acute Coronary Syndrome at Various Stages of the Diagnostic Process: Observational Study.

BACKGROUND: Despite advances in understanding and treating non-ST-elevation acute coronary syndrome (NSTE-ACS), patients continue to experience high r...

MACHINE LEARNING VERSUS PENALISED LOGISTIC REGRESSION FOR PREDICTING IN-HOSPITAL MORTALITY IN RUPTURED ABDOMINAL AORTIC ANEURYSM: A COMPARISON USING A UNIFORM VALIDATION FRAMEWORK.

BACKGROUND: Ruptured abdominal aortic aneurysm (rAAA) remains associated with substantial in-hospital mortality. Although machine-learning methods can...

Development and evaluation of AI-based clinical decision support system for streamlining breast multidisciplinary team meetings.

OBJECTIVES: Multidisciplinary team (MDT) meetings are key to delivering cancer care. Increasing caseload and limited resources make them less effectiv...