Background: Use of contemporary diabetes technologies, including continuous glucose monitoring (CGM) and hybrid closed-loop insulin pumps (HCLs), has expanded rapidly among people with Type 1 diabetes. However, limited research has characterised the impact of hypoglycaemia on quality of life in this population.Methods: We analysed cross-sectional survey data from US adults aged ≥ 18 years who… Continue reading The Impact of Hypoglycaemia on Quality of Life in Adults With Type 1 Diabetes Using Contemporary Diabetes Technologies: A US National Survey
Category: Tech
Voluntary use of LLM-powered virtual standardized patients and medical students’ interview performance: An observational study
Background: Large language model (#LLM )-powered Virtual Standardized Patients (VSPs) offer scalable practice opportunities for clinical interviewing, but their added value within established human Standardized Patient (SP) curricula remains unclear. This study examined how self-directed VSP engagement relates to interview performance on traditional SP assessments through the lens of self-regulated learning theory.Methods: We analyzed VSP… Continue reading Voluntary use of LLM-powered virtual standardized patients and medical students’ interview performance: An observational study
AI-based burn image assessment: Reliability and clinical error patterns of multimodal large language models in a repeated-inference study
Accurate assessment of #burn depth and total body surface area (TBSA) is critical for clinical decision-making; however, it remains subjective and prone to interobserver variability. Multimodal large language models (MLLMs) are increasingly encountered in clinical contexts, but whether these systems can reliably assess burn images remains unclear. We evaluated four MLLMs (GPT-5.4 Pro, Grok 4.1,… Continue reading AI-based burn image assessment: Reliability and clinical error patterns of multimodal large language models in a repeated-inference study
Bridging the Gap: Using an Asynchronous E-Learning Module to Improve Internal Medicine Residents’ Confidence and Preparedness in Ambulatory Blood Pressure Monitoring Interpretation
Background: Ambulatory blood #pressure monitoring (ABPM) is considered a reference standard for diagnosing #hypertension and is recommended for out-of-office blood pressure assessment, yet internal medicine residents receive limited training in its interpretation. We conducted a needs assessment to identify gaps in hypertension and ABPM education and developed an asynchronous e-learning module to address these deficiencies.… Continue reading Bridging the Gap: Using an Asynchronous E-Learning Module to Improve Internal Medicine Residents’ Confidence and Preparedness in Ambulatory Blood Pressure Monitoring Interpretation
Multikingdom microbiome-based machine learning enables multiple sclerosis diagnosis
Emerging evidence suggests a role for the gut bacteria in the pathogenesis of multiple sclerosis (#MS ); however, the role of other microorganisms and their diagnostic potential for MS remain poorly explored. Here, we analyzed large-scale metagenomic data derived from fecal samples (discovery cohort n = 1152; total n = 1306 across 3 geographically diverse… Continue reading Multikingdom microbiome-based machine learning enables multiple sclerosis diagnosis
Data-driven decision support in hospital resource planning: an artificial intelligence-based model proposal for emergency department demand
Background: The sustainability of service quality in healthcare systems is directly related to accurate resource planning, especially in #emergency departments with high unpredictability. This study aims to analyze the impact of meteorological factors on emergency department visits and propose a highly accurate and explainable artificial intelligence-based decision support model for hospital management. Within the scope… Continue reading Data-driven decision support in hospital resource planning: an artificial intelligence-based model proposal for emergency department demand
Novelty to necessity: large language models in clinical practice-a call to action for physicians
Large language models (#LLMs ) represent a rapidly advancing subset of artificial intelligence with significant potential to augment clinical practice. These tools can assist in literature synthesis, documentation, clinical reasoning, and patient communication. Despite demonstrated capabilities, adoption in healthcare remains limited due to barriers such as limited AI literacy, trust concerns, integration challenges, and generational… Continue reading Novelty to necessity: large language models in clinical practice-a call to action for physicians
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 clinical significance, existing risk-prediction models are limited by reliance on in-hospital data that fail to capture complex physiological interactions. Thus, this study aimed to develop and validate an interpretable ensemble machine learning (ML) model integrating both prehospital… Continue reading Machine Learning-Based Prediction Model for Infectious Complications in Trauma and Its Association With In-Hospital Mortality
Using randomization to compare #AI and expert-generated formative assessment questions in medical education
Background: AI-generated content is being used across the education spectrum and is beneficial for creating complex multiple-choice questions, such as those used in medical education. However, evaluating AI-generated content is challenging, and existing testing and evaluation methods are falling short. This study uses randomization to compare medical students' performance on and subjective evaluation of AI… Continue reading Using randomization to compare #AI and expert-generated formative assessment questions in medical education
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