Refractive Surgery

Does the Timing of USMLE Step 1 Affect Performance? A Comparison of Pre- and Post-Clerkship Timing in the Pass/Fail Era.

In 2022, Step 1 of the United States Medical Licensing Examination transitioned to pass/fail scoring, removing a major performance-oriented incentive that historically shaped how and why students prepared for the exam. While Step 1 is typically taken before clerkships, some medical schools have shifted the exam to after core clerkships, citing potential benefits for learning and integration. This ...

Artificial intelligence-driven donor-recipient gut microbiome matching for optimized fecal microbiota transplantation.

Fecal microbiota transplantation (FMT) has emerged as a promising therapy for gastrointestinal diseases, yet its clinical efficacy remains individually variable. Here, we analyze multi-kingdom and functional profiles in pre- and post-FMT metagenomes from 515 FMTs across 30 cohorts and 12 diseases, in which 94 metagenomes from 44 FMTs are newly collected. We reveal a robust association between clin...

RetinalFRNet: retinal vessel segmentation in OCTA images using the feature reconstruction network.

OBJECTIVES: Automated segmentation of retinal blood vessels in optical coherence tomography angiography (OCTA) images is essential for early diagnosis...

Are machine learning models superior to logistic regression models to predict 30-day mortality post-hip fracture surgery?

Hip fractures represent a significant global health burden, with high mortality rates. Accurate prediction of 30-d postoperative mortality is instrume...

Post-operative delirium risk estimation and assessment with machine learning: development and validation of the Open-DREAM Model: A peri-operative dataset analysis.

BACKGROUND: Postoperative delirium (POD) is a common and serious complication in older surgical patients, associated with increased morbidity, prolong...

Volumetric phase-specific three-dimensional radiomics and machine learning for differentiation of non-mass enhancement in breast magnetic resonance imaging.

PURPOSE: Non-mass enhancement (NME) in breast magnetic resonance imaging (MRI) is a diagnostically challenging entity due to overlapping benign and ma...

The Power of Multimodality in Multimodal Large Language Models, Unimodal ChatGPT 5.0, and Human Clinical Experts on a Wound Care Certification Examination: Cross-Sectional Comparative Study.

BACKGROUND: Multimodal large language models (MLLMs) capable of integrating visual and textual information represent a promising advancement for clini...

Deep learning discriminates seizures from normal brain oscillations in the electroencephalogram of a rat model of post-traumatic epilepsy.

This study used machine learning to objectively identify seizures in the electroencephalogram of a model of post-traumatic epilepsy based on fluid per...

Optimizing temporal windows for wearable-augmented post-discharge risk prediction: a methods study.

OBJECTIVE: Traditional readmission risk models relying on static discharge data have limited predictive performance and fail to capture patients' reco...

A Bibliometric Analysis of Artificial Intelligence and Digital Technologies in Maxillofacial Skeletal Reconstruction: Current Applications and Research Trends.

BACKGROUND: Maxillofacial skeletal defects lead to functional impairments, aesthetic disfigurement, and psychosocial burdens, while traditional surgic...

Machine learning identifies pupil size and corneal thickness as key predictors of axial elongation rate.

PURPOSE: This study aimed to develop a machine learning-based prediction model for myopia progression using ocular biometric parameters to provide an ...

Classifying Post-COVID "Brain Fog" Patients and Identifying Key ROIs via Graph Neural Network Model.

Brain fog has raised significant public health concerns as a common neurocognitive impairment in the post-COVID-19 condition, involving memory loss, p...

Understanding Perceived Forehead Changes Post-Endoscopic Brow Lift: The Impact of Frontalis Muscle Contraction.

BACKGROUND: Endoscopic brow lift surgery is increasingly performed, yet there are concerns about potential increase in forehead height and hairline po...

Predicting Keratoconus Progression From a Single Visit: Is Machine Learning Successful?

PURPOSE: To develop and evaluate a machine learning (ML)-based model for predicting keratoconus (KCN) progression in an Iranian cohort. METHODS: This ...

Personalized Treatment Selection for Digital Eating Disorder Interventions: A Proof-of-Concept for the Personalized Advantage Index.

OBJECTIVE: Although a range of evidence-based treatments for eating disorders exist, treatment response varies substantially. The ability to match ind...

HRS scientific statement on artificial intelligence integration framework into clinical electrophysiology workflows.

Artificial intelligence (AI) and digital health technologies (DHTs) are rapidly transforming health care, offering unprecedented opportunities for enh...

From prediction to practice: early implementation of a machine learning-based hospitalization prediction tool in the emergency department.

BACKGROUND: Machine learning (ML) models can accurately predict hospital admissions in emergency departments (EDs), but real-world adoption remains ra...

Correction: Artificial intelligence in pediatric myopia-a narrative review.

Large language model-assisted radiology reporting in a single-radiologist implementation: a retrospective cohort study interpreted through a UTAUT lens.

BACKGROUND: Radiologist burnout affects approximately 40% of US radiologists. Large language models (LLMs) may improve workflow efficiency, but real-w...

Deep learning driven clustering of post-traumatic stress disorder (PTSD) profiles in student-athletes: implications for precision and stratified mental health support.

BACKGROUND: Athletes frequently experience potentially traumatic events related to sports injuries, significantly increasing their risk of developing ...