The prevalence of diabetic retinopathy in Indigenous and non-Indigenous populations in Canada: systematic review and meta-analysis.

Objective: To estimate the prevalence of diabetic retinopathy (DR) in Canada and to explore possible differences between Indigenous and non-Indigenous Canadians with diabetes.

Design: Systematic review and meta-analysis.

Methods: The Ovid MEDLINE, EMBASE, and Web of Science Databases were searched.

An eyecare foundation model for clinical assistance: a randomized controlled trial.

In the context of an increasing need for clinical assessments of foundation models, we developed EyeFM, a multimodal vision-language eyecare copilot, and conducted a multifaceted evaluation, including retrospective validations, multicountry efficacy validation as a clinical copilot and a double-masked randomized controlled trial (RCT). EyeFM was pretrained on 14.5 million ocular images from five imaging modalities paired with clinical texts from global, multiethnic datasets.

Generative Artificial Intelligence Tools in Medical Research (GAMER): Protocol for a Scoping Review and Development of Reporting Guidelines.

Background: The integration of artificial intelligence (AI) has revolutionized medical research, offering innovative solutions for data collection, patient engagement, and information dissemination. Powerful generative AI (GenAI) tools and other similar chatbots have emerged, facilitating user interactions with virtual conversational agents. However, the increasing use of GenAI tools in medical research presents challenges, including ethical concerns, data privacy issues, and the potential for generating false content.

Multi-Comparison of Different Ocular Imaging Modality-based Deep Learning Models for Visually Significant Cataract Detection.

Purpose: Age-related cataract is the leading cause of vision impairment. Researchers have utilized various imaging modalities, including slit beam, diffuse anterior segment, and retinal imaging, to develop deep learning (DL) algorithms for automated cataract analysis. However, the comparative performance of these algorithms across different ocular imaging modalities remains unevaluated, mainly due to the absence of standardized test sets across studies.

Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding.

Medical phrase grounding is crucial for identifying relevant regions in medical images based on phrase queries, facilitating accurate image analysis and diagnosis. However, current methods rely on manual extraction of key phrases from medical reports, reducing efficiency and increasing the workload for clinicians. Additionally, the lack of model confidence estimation limits clinical trust and usability.

Ophthalmological Question Answering and Reasoning Using OpenAI o1 vs Other Large Language Models.

Importance: OpenAI's recent large language model (LLM) o1 has dedicated reasoning capabilities, but it remains untested in specialized medical fields like ophthalmology. Evaluating o1 in ophthalmology is crucial to determine whether its general reasoning can meet specialized needs or if domain-specific LLMs are warranted.

Objective: To assess the performance and reasoning ability of OpenAI's o1 compared with other LLMs on ophthalmological questions.

AI Workflow, External Validation, and Development in Eye Disease Diagnosis.

Importance: Timely disease diagnosis is challenging due to limited clinical availability and growing burdens. Although artificial intelligence (AI) has shown expert-level diagnostic accuracy, a lack of downstream accountability, including workflow integration, external validation, and further development, continues to hinder its clinical adoption.

Objective: To address gaps in the downstream accountability of medical AI through a case study on age-related macular degeneration (AMD) diagnosis and severity classification.

Generation of multidisease fundus photographs with code-free platform.

Purpose: To generate fundus photographs of multiple kinds of retinal disease, bypassing the requirement of coding technique.

Methods: The dataset contained fundus photographs of 10 categories of retinal conditions, with 500 fundus photographs in each category. We randomly divided the collected data into a training set (80%) and a test set (20%).

Grip Strength and Age-Related Ocular Diseases: Insights from Observational, Mendelian Randomization, and Mediation Analyses.

Objective: We aimed to examine the cross sectional and causal associations of grip strength with cataract, glaucoma, diabetic retinopathy (DR), and age-related macular degeneration (AMD) and probe the underlying mechanisms by evaluating the mediating role of metabolomic alterations.

Design: Cross sectional study.

Subjects: A total of 307 796 UK Biobank participants with grip strength and covariates data available.

Evaluation and comparison of large language models' responses to questions related optic neuritis.

Objectives: Large language models (LLMs) show promise as clinical consultation tools and may assist optic neuritis patients, though research on their performance in this area is limited. Our study aims to assess and compare the performance of four commonly used LLM-Chatbots-Claude-2, ChatGPT-3.5, ChatGPT-4.

Generative artificial intelligence for fundus fluorescein angiography interpretation and human expert evaluation.

Fundus fluorescein angiography (FFA) is the gold standard for diagnosing chorioretinal diseases, but its interpretation requires significant expertise and time. Despite generative AI's enormous potential in medical report generation, automatic FFA interpretation lacks robust models and sufficient evaluation metrics. This study introduces InterpreFFA, a diagnosis-supervised contrastive learning framework, to emulate ophthalmologists' decision-making process in FFA report generation.

Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model.

Previous foundation models for fundus images were pre-trained with limited disease categories and knowledge base. Here we introduce RetiZero, a vision-language model that incorporates knowledge from over 400 fundus diseases. The model is pre-trained on 341,896 fundus images with accompanying text descriptions gathered from diverse sources across multiple ethnicities and countries.

A deep learning system for detecting systemic lupus erythematosus from retinal images.

Systemic lupus erythematosus (SLE) is a serious autoimmune disorder predominantly affecting women. However, screening for SLE and related complications poses significant challenges globally, due to complex diagnostic criteria and public unawareness. Since SLE-related retinal involvement could provide insights into disease activity and severity, we develop a deep learning system (DeepSLE) to detect SLE and its retinal and kidney complications from retinal images.

Myopia and high myopia trends in Chinese children and adolescents over 25 years: a nationwide study with projections to 2050.

Background: The global rise in myopia, particularly in Asia, presents significant public health challenges. Analyzing trends and forecasting impacts are critical for developing strategies to mitigate this burden.

Methods: We conducted the largest study to date on myopia and high myopia prevalence in Chinese children and adolescents aged 7-18 years, analyzing data from 5,095,256 individuals across 119 studies from 1998 to 2022.

Current status of school vision screening-rationale, models, impact and challenges: a review.

Uncorrected refractive error is the leading cause of vision impairment in children globally, and studies have demonstrated that spectacle correction addresses the large majority of childhood vision impairment. Furthermore, trial evidence illustrates the beneficial impact of spectacles on learning, with effect sizes exceeding that of other school health interventions. While it is established that good vision is important for learning and optimising childhood development and quality of life, many countries lack healthcare systems that provide vision screening or universal access to eyecare for all citizens.

An adaptive AI-based virtual reality sports system for adolescents with excess body weight: a randomized controlled trial.

Adolescents with obesity face numerous health risks and encounter barriers that lead to physical inactivity. We developed a virtual reality sports system, named REVERIE (Real-World Exercise and VR-Based Exercise Research in Education), which used deep reinforcement learning to train transformer-based virtual coaching agents, offering immersive and effective sports guidance, with biomechanical performance comparable to real-world physical sports. We integrated REVERIE into a randomized controlled trial involving an 8-week intervention in adolescents with excess body weight (n = 227).

Diabetic Retinopathy Assessment through Multitask Learning Approach on Heterogeneous Fundus Image Datasets.

Objective: To develop and validate an artificial intelligence (AI)-based system, Diabetic Retinopathy Analysis Model Assistant (DRAMA), for diagnosing diabetic retinopathy (DR) across multisource heterogeneous datasets and aimed at improving the diagnostic accuracy and efficiency.

Design: This was a cross-sectional study conducted at Zhejiang University Eye Hospital and approved by the ethics committee.

Subjects: The study included 1500 retinal images from 957 participants aged 18 to 83 years.

The acceptance of ophthalmic artificial intelligence for eye diseases: a literature review and qualitative analysis.

Thorough investigations of end-users' awareness, acceptance, and concerns about ophthalmic artificial intelligence (AI) are essential to ensure its successful implementation. We conducted a literature review on the acceptance of ophthalmic AI to provide an overall insight and qualitatively analysed the quality of eligible studies using a psychological model. We identified sixteen studies and evaluated these studies based on four primary factors (i.

Relationship of Choroidal Thickness With Age-Related Macular Degeneration in Asians: An Asian Eye Epidemiology Consortium Meta-Analysis.

Purpose: To compare the choroidal thickness (CT) of participants with various stages of age-related macular degeneration vs. normal controls through a meta-analysis of studies conducted within the Asian Eye Epidemiology Consortium.

Methods: Eight population-based studies from China, Iran, Japan, and Singapore were included.

A deep learning system for detecting silent brain infarction and predicting stroke risk.

Current brain imaging to detect silent brain infarctions (SBIs) is not feasible for the general population. Here, to overcome this challenge, we developed a retinal image-based deep learning system, DeepRETStroke, to detect SBI and refine stroke risk. We use 895,640 retinal photographs to pretrain the DeepRETStroke system, which encodes a domain-specific foundation model for representing eye-brain connections.

A Comparative Validation Study of Near Visual Acuity Assessment Using Different Handheld Acuity Charts.

Objective: To develop a conversion table and compare the cross-validity of 3 types of widely utilized near vision charts: the ETDRS near chart, the N-notation chart, and the Rosenbaum chart.

Design: A prospective, cross-sectional, comparative validation study.

Participants: Aged ≥40 years.

Knowledge and Awareness of Generative Artificial Intelligence Use in Medicine Among International Stakeholders: A Cross-Sectional Study.

Objective: To assess the knowledge, attitudes, and practices (KAP) of medical stakeholders regarding the use of generative artificial intelligence (GAI) tools.

Methods: A cross-sectional survey was conducted among stakeholders in medicine. Participants included researchers, clinicians, and medical journal editors with varying degrees of familiarity with GAI tools.

Vision transformer-based stratification of pre/diabetic and pre/hypertensive patients from retinal photographs for 3PM applications.

Objective: Diabetes and hypertension pose significant health risks, especially when poorly managed. Retinal evaluation though fundus photography can provide non-invasive assessment of these diseases, yet prior studies focused on disease presence, overlooking control statuses. This study evaluated vision transformer (ViT)-based models for assessing the presence and control statuses of diabetes and hypertension from retinal images.

Digital Therapeutics in China: Comprehensive Review.

Background: Digital therapeutics (DTx) are software-driven interventions that provide personalized, evidence-based treatments for various medical conditions. China's rapid technological adoption, large population, and supportive government policies position it as a potential global leader in DTx. However, challenges remain in clinical trial standardization, regulatory approval, product development, and reimbursement models.