Ophthalmology

Rethinking scale in ophthalmic artificial intelligence: from bigger models to smarter clinical reasoning.

Recent advances in ophthalmic AI have improved benchmark performance, yet clinical trust remains limited. We argue that progress should move beyond data and model scaling toward trustworthy, skill-efficient systems that integrate multimodal evidence, external knowledge, and uncertainty-aware reasoning. Ophthalmology provides a strong testbed for agentic AI, but safe clinical translation will requi...

Landscape fragmentation drives rice heavy-metal health risk and reveals actionable thresholds.

The contamination of staple crops with heavy metals is predominantly assessed through a geochemical lens, treating farmland as a static container while overlooking the organizing influence of landscape structure. Here we advance a "landscape risk filter" perspective and a threshold-identification workflow that links landscape configuration to spatially explicit health risk. Using Jiangxi Province ...

A deep learning model for automated identification of age-related macular degeneration atrophy.

Age-related macular degeneration (AMD), a leading cause of visual impairment and blindness among the elderly, is projected to affect 288 million indiv...

Fear and Prompting: AI, Originary Technicity, and Colonialism.

This paper examines psychoanalysts' affective and defensive responses to artificial intelligence (AI) through the lens of originary technicity, the ph...

Advanced computer-aided diagnosis for age-related macular degeneration: integrating segmentation and feature extraction for precise diagnosis.

Age-related Macular Degeneration (AMD) is a leading cause of vision loss globally; necessitating early detection and precise diagnosis for effective i...

Comparative analysis of large language models and clinicians in thyroid eye disease using structured questionnaires.

To preliminarily evaluate agreement between large language models (LLMs) and ophthalmic clinicians regarding thyroid eye disease (TED) clinical decisi...

Automated phenotyping of ophthalmologic diseases from routine medical records using small language models and the human phenotype ontology (HPO).

Automated phenotyping in ophthalmology requires accurate standardization of clinical terms to facilitate interoperability and research. This study eva...

An optimal Petrov-Galerkin framework for operator networks.

The optimal Petrov-Galerkin formulation to solve partial differential equations (PDEs) recovers the best approximation in a specified finite-dimension...

Global approval and certification of ophthalmic AI devices: A comparative regulatory perspective.

Artificial intelligence (AI) tools are rapidly reshaping ophthalmology by improving screening and diagnosis for diabetic retinopathy, age-related macu...

Surgical technology update for ophthalmic surgery: heads-up display, artificial intelligence, instrumentation, and robotics.

PURPOSE OF REVIEW: Rapid advances in surgical visualization, microsurgical instrumentation, artificial intelligence (AI), and robotic assistance are r...

Update on topography and tomography for refractive surgery.

PURPOSE OF REVIEW: To review and summarize the current literature on recent advances in corneal topography and anterior segment tomography, highlighti...

OCT-based AI-assisted phenotyping of intermediate AMD in the prospective PINNACLE trial: PINNACLE Study Report 9.

BACKGROUND/AIMS: PINNACLE is one of the largest prospective multicentre observational studies evaluating the progression of intermediate age-related m...

Correction: Gene expression and machine learning techniques uncover corneal biomarkers associated with oxidative stress in the myopia progression.

Artificial intelligence algorithm for real-time detection and counting of Trypanosoma cruzi parasites using smartphone microscopy.

Chagas disease affects 6-7 million people worldwide and causes approximately 12,000 deaths annually. Diagnostic methods vary by disease stage, with se...

Evaluation of large language models and retrieval-augmented generation for clinical reasoning in pediatric myopia: a 50-case real-world study.

To evaluate the clinical reasoning ability of large language models (LLMs) and retrieval-augmented generation (RAG) systems in pediatric myopia manage...

Financial incentives increase uptake and perceived effectiveness of autonomous medical AI, yet patients still seek human reconfirmation.

Artificial intelligence (AI) is increasingly used for diagnostic screening. In diabetic retinopathy screening, autonomous AI systems can identify dise...

Shared representations in brains and models reveal a two-route cortical organization during scene perception.

The brain transforms visual inputs into cortical representations that support diverse cognitive and behavioral goals. Characterizing how this informat...

Smartphone-based lightweight AI system for real-time multiple anterior segment disease screening: development and real-world validation.

BACKGROUND: Anterior segment diseases are a major global cause of preventable blindness, especially in regions with limited access to specialized opht...

A Modular and Scalable FPGA Platform for Intelligent, High-Throughput Image-Activated Cell Sorting.

Image-activated cell sorting (IACS) enables high-throughput cell classification by linking cellular morphology to physiology. While integrating advanc...

Artificial intelligence in retinoblastoma and uveal melanoma: a narrative review.

INTRODUCTION: Artificial intelligence (AI) is reshaping diagnostic paradigms across oncology. In ophthalmic oncology encompassing conditions like reti...