Ophthalmology

Refractive Surgery

Latest AI and machine learning research in refractive surgery for healthcare professionals.

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The Universal Normal Embedding

Generative models and vision encoders have largely advanced on separate tracks, optimized for differ...

CornOrb: A Multimodal Dataset of Orbscan Corneal Topography and Clinical Annotations for Keratoconus Detection

In this paper, we present CornOrb, a publicly accessible multimodal dataset of Orbscan corneal topog...

Automatic Configuration of LLM Post-Training Pipelines

LLM post-training pipelines that combine supervised fine-tuning and reinforcement learning are diffi...

Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos

The transition from image to video understanding requires vision-language models (VLMs) to shift fro...

Multimodal Molecular Mapping of the Vasculature in Human Cortex Reveals Lipid Markers of Cerebral Amyloid Angiopathy

Cerebral amyloid angiopathy (CAA) commonly co-occurs with Alzheimer's disease (AD), yet the molecula...

Informative Perturbation Selection for Uncertainty-Aware Post-hoc Explanations

Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models m...

Informative Perturbation Selection for Uncertainty-Aware Post-hoc Explanations

Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models m...

Rethinking Machine Unlearning: Models Designed to Forget via Key Deletion

Machine unlearning is rapidly becoming a practical requirement, driven by privacy regulations, data ...

Can Parents and Patients Understand Myopia Using Large Language Model-Based Chatbots?

Purpose: This study aimed to compare the reliability of myopia-related information from AI chatbots ...

A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic

Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and man...

A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic

Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and man...

AI-Generated Responses to Patient's Messages: Effectiveness, Feasibility and Implementation

Background Generative artificial intelligence (GenAI) in healthcare may reduce administrative burden...

Fed-ADE: Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift

Federated learning (FL) in post-deployment settings must adapt to non-stationary data streams across...

Neural Image Space Tessellation

We present Neural Image-Space Tessellation (NIST), a lightweight screen-space post-processing approa...

Interpretable machine-learning model for cataract associated factors identifying in patients with high myopia

Purpose: To systematically evaluate ocular biometric and systemic laboratory factors associated with...

Space Syntax-guided Post-training for Residential Floor Plan Generation

Pre-trained generative models for residential floor plans are typically optimized to fit large-scale...

Causal Decoding for Hallucination-Resistant Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) deliver detailed responses on vision-language tasks, yet re...

Resting-state Compensatory Remapping in Patients with Brain Tumour Before and After Surgery

Brain tumours invade neural tissue, disrupting the functional organisation of neural networks. This ...

Life-course comorbidity patterns and integrated prediction of postpartum depression, multimorbidity, and symptom progression

Perinatal depression (PD) is common and disabling, yet its longitudinal comorbidity patterns and pre...

Rare Event Early Detection: A Dataset of Sepsis Onset for Critically Ill Trauma Patients

Sepsis is a major public health concern due to its high morbidity, mortality, and cost. Its clinical...

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