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http://dx.doi.org/10.3899/jrheum.2023-0617 | DOI Listing |
Objectives: To investigate whether quantitative retinal markers, derived from multimodal retinal imaging, are associated with increased risk of mortality among individuals with proliferative diabetic retinopathy (PDR), the most severe form of diabetic retinopathy.
Design: Longitudinal retrospective cohort analysis.
Setting: This study was nested within the AlzEye cohort, which links longitudinal multimodal retinal imaging data routinely collected from a large tertiary ophthalmic institution in London, UK, with nationally held hospital admissions data across England.
J Clin Epidemiol
September 2025
Health Evidence Synthesis, Recommendations and Impact, School of Public Health, University of Adelaide, South Australia.
Background And Objectives: Evidence syntheses systematically compile and analyze information from multiple sources to support health care decision making. As many different types of questions need to be answered in health care, different evidence synthesis types have emerged. In this article, we introduce the most common types of evidence synthesis.
View Article and Find Full Text PDFJAMA Ophthalmol
September 2025
Department of Ophthalmology, University of Montreal, Montreal, Quebec, Canada.
Importance: Large language models (LLMs) are increasingly being explored in clinical decision-making, but few studies have evaluated their performance on complex ophthalmology cases from clinical practice settings. Understanding whether open-weight, reasoning-enhanced LLMs can outperform proprietary models has implications for clinical utility and accessibility.
Objective: To evaluate the diagnostic accuracy, management decision-making, and cost of DeepSeek-R1 vs OpenAI o1 across diverse ophthalmic subspecialties.
PLoS One
September 2025
Department of Electrical Engineering and Mechatronics, Faculty of Engineering, University of Debrecen, Debrecen, Hungary.
Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans.
View Article and Find Full Text PDF