Health

All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

339 stories · page 2 of 23

Evidence-backed gain

A Risk Prediction Model for Normal-Tension Glaucoma Progression Integrating Genetic Markers and Corneal Biomechanics

Objective Normal-tension glaucoma (NTG) is characterized by progressive optic nerve damage despite intraocular pressure remaining consistently within the normal range. Predicting disease progression in patients with confirmed NTG remains challenging. This study aimed to develop and validate an interpretable machine learning model integrating genetic risk scores and corneal biomechanical parameters to predict progression risk in patients with NTG, identify independent predictors, and quantify the contribution of…

Seminars in Ophthalmology · Health

Both readings

An Umbrella Review of Artificial Intelligence Applications in Mental Health Care

Background Artificial intelligence (AI) is increasingly used in mental health care to address rising demand, workforce shortages and access barriers; however, evidence remains scattered across multiple systematic reviews, limiting synthesis and practical application. Objective To synthesise evidence on AI applications in mental health care, including trends, uses, benefits, challenges and risk-mitigation strategies, guided by an ethics of care framework. Methods This umbrella review of systematic reviews followe…

Health

Evidence-backed gain

Targeting GLS and LPIN2 in renal fibroblasts: potential therapeutic targets for kidney stone disease identified by integrated multi-omics analysis

Kidney stones (KS) are a common urological condition, the aetiology of which remains incompletely understood. This study aimed to investigate the key cell types involved in the formation of kidney stones and the molecular mechanisms associated with calcium metabolism. Single-cell and bulk RNA-seq datasets related to kidney stones were downloaded from the GEO database. Single-cell analysis was performed to explore the heterogeneity of kidney stones and identify differentially expressed genes (DEGs). Candidate gen…

Health

Both readings

The Lymph Node Ratio as a Predictive Biomarker for Individualized Benefit from Adjuvant Chemotherapy in Gastric Cancer: A Retrospective Cohort and Causal Machine Learning Study

Background Optimizing adjuvant chemotherapy (AC) for gastric cancer (GC) remains challenging due to patient heterogeneity. While the lymph node ratio (LNR) is a known prognostic factor, its role in predicting individualized AC benefit remains underexplored. This study aimed to leverage causal machine learning to explore LNR's role for personalized treatment. Methods We conducted a retrospective cohort study of 2,748 patients undergoing radical gastrectomy (2007-2017, re-staged by AJCC 8th edition). While the ful…

Health

Evidence-backed gain

Integrative single-cell and machine-learning analysis identifies LGALS1 as a macrophage-associated diagnostic and prognostic biomarker in hepatocellular carcinoma

Background Hepatocellular carcinoma (HCC) is characterized by marked heterogeneity and an immunosuppressive microenvironment in which tumor-associated macrophages contribute to disease progression. This study aimed to identify macrophage-associated biomarkers with diagnostic, prognostic, and translational relevance in HCC. Methods Single-cell RNA-sequencing datasets were integrated with bulk transcriptomic and clinical data from TCGA-LIHC, GEO, and ICGC cohorts. Macrophage markers were intersected with tumor-ass…

Health

Evidence-backed gain

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…

Health

Both readings

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

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

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

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

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

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

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

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

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