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.

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.

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).

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.

OCTA as a reliable prognostic tool for active NVD treated with Panretinal Photocoagulation and/or ranibizumab.

Purpose: To compare the prognosis of neovascularization of the disc (NVD) after panretinal photocoagulation (PRP) and/or ranibizumab treatment, based on OCT angiography (OCTA) patterns.

Methods: In this prospective study, treatment-naive patients with stage IV diabetic retinopathy (DR) and NVD were imaged with 6x6 mm2 OCTA scans. NVD was classified according to OCTA morphological features: different sources (retinal arteries and veins), different activities (exuberant vascular proliferation (EVP)+ and EVP-) and different configurations (type I&II, III and IV).

Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study.

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images.

Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images.

AI-assisted facial analysis in healthcare: From disease detection to comprehensive management.

Medical conditions and systemic diseases often manifest as distinct facial characteristics, making identification of these unique features crucial for disease screening. However, detecting diseases using facial photography remains challenging because of the wide variability in human facial features and disease conditions. The integration of artificial intelligence (AI) into facial analysis represents a promising frontier offering a user-friendly, non-invasive, and cost-effective screening approach.

Large language models for diabetes training: a prospective study.

Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models (LLMs) provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: ChatGPT-3.

A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms.

Importance: Myopic maculopathy (MM) is a major cause of vision impairment globally. Artificial intelligence (AI) and deep learning (DL) algorithms for detecting MM from fundus images could potentially improve diagnosis and assist screening in a variety of health care settings.

Objectives: To evaluate DL algorithms for MM classification and segmentation and compare their performance with that of ophthalmologists.

Artificial intelligence for diabetes care: current and future prospects.

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures.

Improve Cardiac Emergency Preparedness by Building a Team-Based Cardiopulmonary Resuscitation Educational Plan.

Objective: To design an innovative team-based cardiopulmonary resuscitation (CPR) educational plan for multiple bystanders and evaluate whether it was associated with better teamwork and higher quality of resuscitation.

Methods: The team-based CPR plan defined the process for a three-person team, emphasize task allocation, leadership, and closed-loop communication. Participants qualified for single-rescuer CPR skills were randomized into teams of 3.