+ HEALTH Background Discharge letters are essential for continuity of care, yet patients often misunderstand their con…± HEALTH Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise…+ CLIMATE Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Co…± HEALTH Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains chal…+ HEALTH T cell exhaustion has widespread implications for the progression and treatment of chronic diseases including…+ HEALTH Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated w…+ HEALTH Objectives To map and synthesize the scientific evidence regarding the use of Augmented Reality (AR) and Mixe…± HEALTH Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovas…
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Identifying risk factors for marijuana use among male high school students using machine learning: Implications for public health

Objectives To identify risk and protective factors associated with lifetime marijuana use among male high school students through an interpretable machine learning model, providing evidence to support early and targeted public health interventions. Study design Cross-sectional analysis of 2023 Youth Risk Behavior Surveillance System (YRBS) data for boys in grades 9-12 across the United States. Methods The final analytical sample included 8285 boys after excluding missing outcomes. Thirty-six predictors spanning…

Public Health · Health

Identifying risk factors for marijuana use among male high school students using machine learning: Implications for public health
Triglyceride-glucose frailty index, metabolic-frailty phenotypes, and mortality in critically ill patients with acute kidney injury: Derivation, interpretation, and external validation
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Triglyceride-glucose frailty index, metabolic-frailty phenotypes, and mortality in critically ill patients with acute kidney injury: Derivation, interpretation, and external validation

Background Prognosis remains heterogeneous among critically ill patients with acute kidney injury (AKI). We evaluated the triglyceride-glucose frailty index (TyG-FI), a composite of metabolic burden and laboratory-based frailty, for mortality risk characterization, phenotype identification, prediction, and external validation. Methods We included 2230 adults with KDIGO-defined AKI from MIMIC-IV. Associations between TyG-FI and ICU, in-hospital, 28-day, 90-day, and 365-day mortality were assessed using multivaria…

Health
Combining Statistical Modeling and Machine Learning for Prognostic Feature Selection in Cervical Cancer: A Retrospective Study Based on SEER 2004 to 2015 Data
Evidence-backed gain

Combining Statistical Modeling and Machine Learning for Prognostic Feature Selection in Cervical Cancer: A Retrospective Study Based on SEER 2004 to 2015 Data

Objectives Cervical cancer is a leading female malignancy with high global morbidity/mortality, and remains high recurrence risk after standard treatment. Accurate prognostic feature identification is critical for personalized therapy and patient survival improvement, while traditional indicators and single biomarkers lack sufficient accuracy in prognostic prediction. Methods In this population-based retrospective study, we analyzed 5392 patients with cervical squamous cell carcinoma from the surveillance, epide…

Health
Artificial intelligence-driven transformation in healthcare: the mediating role of technostress in the impact of openness to organizational change and attitude toward AI on innovative behavior
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Artificial intelligence-driven transformation in healthcare: the mediating role of technostress in the impact of openness to organizational change and attitude toward AI on innovative behavior

Purpose The rapid proliferation of artificial intelligence (AI) applications in healthcare is transforming the ways in which employees interact with technology, making it crucial to understand the effects of this process on employee behavior. The purpose of this study is to examine the effects of openness to organizational change and attitudes toward AI on employees' innovative behavior, and to test the mediating role of technostress in these effects. Design/methodology/approach The research was conducted using…

Health
Comparative evaluation of feature-selection strategies for machine learning-based histological grading of breast cancer using tumor and fibroglandular-tissue DCE-MRI features
Evidence-backed problem

Comparative evaluation of feature-selection strategies for machine learning-based histological grading of breast cancer using tumor and fibroglandular-tissue DCE-MRI features

Nottingham histological grading is central to breast cancer prognosis and treatment planning, but conventional pathological assessment is labor-intensive and subject to inter-observer variability. Radiomics and machine learning may support noninvasive preoperative grade prediction. To compare mutual-information, chi-squared, and LASSO feature-selection strategies for binary Nottingham grade classification using tumor- and fibroglandular-tissue (FGT)-derived DCE-MRI features. This retrospective secondary analysis…

Health
A large language models-assisted and expert-corrected workflow for preoperative anesthesia assessment drafts: A single-centre exploratory feasibility study
Evidence-backed problem

A large language models-assisted and expert-corrected workflow for preoperative anesthesia assessment drafts: A single-centre exploratory feasibility study

Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications. This study aimed to describe the feasibility and perceived usefulness of a single-centre, expert-corrected LLM workflow for preparing preoperative anesthesia assessment drafts for complex consultation cases. Secondary aims were to describe error patterns identified by anesthesiologists and to explore residents' perceptions…

Health
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
Evidence-backed problem

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

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, experiences and overall care pathways in the real-world clinical setting is limited. We will conduct a prospective cluster randomised cro…

Health

Artificial Intelligence-Assisted Chest Radiography: A Prospective Crossover Multi-Reader Study on Diagnostic Performance and Workflow Efficiency

To evaluate the impact of four commercially available AI solutions for chest radiography on diagnostic performance, workflow efficiency, and clinical decision-making in a real-world setting. In this prospective, monocentric, crossover reader study, five readers (one to six years of experience) assessed 1200 consecutive patients undergoing chest radiography (1861 total radiographs) for pulmonary infiltrates, pleural effusions, mediastinal masses, pneumothorax, and pulmonary nodules under five conditions: without…

Health
Artificial Intelligence-Assisted Chest Radiography: A Prospective Crossover Multi-Reader Study on Diagnostic Performance and Workflow Efficiency

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 corresponding periapical radiograph, were independently submitted to each AI software program using a standardized prompt…

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

An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy

Background Laryngeal cancer is a significant global health issue with high mortality, and early diagnosis is critical for survival. Developing accurate diagnostic models for laryngoscopy can reduce potential repeated biopsies and lessen the patient burden, representing an urgent clinical need. However, existing artificial intelligence models often function as black boxes and are trained on single, pre-selected images, which does not reflect the clinical workflow where multiple images are assessed. Methods We con…

Health
An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy

Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction

Background Assessment of pulmonary vascularity on chest radiographs (CXRs) in congenital heart disease (CHD) is limited by subjectivity, and existing criteria lack sufficient validation. Artificial intelligence-based deep learning model (DLM) analysis may improve accuracy. We developed and validated structured criteria and a DLM to evaluate pulmonary vascularity and compared it with conventional clinical interpretation. Methods A total of 400 deidentified CXRs from CHD patients with Fick-derived pulmonary-to-sys…

Health
Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction

Explainable artificial intelligence in medical imaging: how to interpret, evaluate, and use artificial intelligence explanations

Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust. To address this, a growing body of methods has been developed to help clinicians understand and evaluate AI predictions. This field, known as explainable AI (XAI), aims to help clinicians interrogate, interpret, and critically evaluate AI predictions by identifying factors associated with model outputs…

Health
Explainable artificial intelligence in medical imaging: how to interpret, evaluate, and use artificial intelligence explanations

EXPRESS: Relation between Albumin-Corrected Anion Gap and In-Hospital Mortality in Patients with Traumatic Lung Injury: A Multicenter Retrospective Cohort Study and the Development of Machine Learning-Based Prediction Models

Background The anion gap is primarily utilized as an indicator for evaluating acid-base imbalances in critically ill patients. However, its accuracy is reduced in such patients due to low albumin levels. The albumin-corrected anion gap (ACAG) enhances the accuracy of assessing acid-base imbalances. Individuals with traumatic lung injury (TLI) in the intensive care unit (ICU) often have severe metabolic acidosis and hypoalbuminemia. Nevertheless, the association of ACAG with the prognosis of patients with TLI is…

Health
EXPRESS: Relation between Albumin-Corrected Anion Gap and In-Hospital Mortality in Patients with Traumatic Lung Injury: A Multicenter Retrospective Cohort Study and the Development of Machine Learning-Based Prediction Models

AI patients, real practice: exploring the use of AI-simulated patients to support primary healthcare training for combat medical technicians

Introduction Combat Medical Technicians (CMTs) are central to military primary care but have limited opportunity for clinical exposure. Simulated patients offer a controlled method to maintain clinical currency. Advances in conversational artificial intelligence (AI) enable realistic and interactive simulated consultations. We present our evaluation of the feasibility, acceptability and educational impact of AI-simulated patients for CMT training. Methods Five military primary care simulated patients were develo…

Health
AI patients, real practice: exploring the use of AI-simulated patients to support primary healthcare training for combat medical technicians

Evaluation of Differential Diagnosis of Odontogenic Lesions in Cone Beam Computed Tomography Images Using Radiomics-Based Machine Learning

Objective This study aimed to evaluate the structural characteristics of mandibular alveolar bone in patients with Type 1 diabetes mellitus (T1DM), Type 2 diabetes mellitus (T2DM), and systemically healthy controls using panoramic radiography-based radiomic analysis combined with machine learning algorithms. Materials and methods A total of 225 panoramic radiographs (75 T1DM, 75 T2DM, 75 healthy controls) were retrospectively analyzed. ROIs were segmented from eight anatomical mandibular segments per subject, an…

Health
Evaluation of Differential Diagnosis of Odontogenic Lesions in Cone Beam Computed Tomography Images Using Radiomics-Based Machine Learning