Latest AI and machine learning research in laser surgery for healthcare professionals.
We present CataractSAM-2, a domain-adapted extension of Meta's Segment Anything Model 2, designed for real-time semantic segmentation of cataract ophthalmic surgery videos with high accuracy. Positioned at the intersection of computer vision and medical robotics, CataractSAM-2 enables precise intraoperative perception crucial for robotic-assisted and computer-guided surgical systems. Furthermore, ...
Artificial intelligence (AI) is increasingly used in clinical settings, yet limited oversight and domain expertise can allow algorithmic bias and safety risks to persist. This study evaluates whether an agentic AI system can support auditing biomedical machine learning models for fairness in early-onset colorectal cancer (EO-CRC), a condition with documented demographic disparities. We implemented...
The Segment Anything Model (SAM) has revolutionized interactive segmentation through spatial prompting. While existing work primarily focuses on autom...
Catheter-based interventions are widely used for the diagnosis and treatment of cardiac diseases. Recently, robotic catheters have attracted attention...
Clinically reliable perception of surgical scenes is essential for advancing intelligent, context-aware intraoperative assistance such as instrument h...
Objective To characterize the clinical and administrative concerns communicated through secure ophthalmology messaging and to assess differences in me...
Retinal diseases spanning a broad spectrum can be effectively identified and diagnosed using complementary signals from multimodal data. However, mult...
Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML model...
Clearing Assisted Scattering Tomography (CAST) extends coherent scattering tomography to whole-brain imaging, enabling visualization of fine-scale bra...
Medical image classification is a core task in computer-aided diagnosis (CAD), playing a pivotal role in early disease detection, treatment planning, ...
Retinal fundus photography is indispensable for ophthalmic screening and diagnosis, yet image quality is often degraded by noise, artifacts, and uneve...
ImportanceVision-language models (VLMs) enable generalist multimodal reasoning, but their ability to resolve brief, low-contrast cues in surgical vide...
INTRODUCTION: Advances in artificial intelligence offer the promise of automated analysis of optical coherence tomography (OCT) scans to detect ocular...
Multimodal ophthalmic imaging-based diagnosis integrates color fundus image with optical coherence tomography (OCT) to provide a comprehensive view ...
low-dose computed tomography (LDCT) images suffer from severe noise due to reduced radiation exposure. Most existing deep learning-based denoising met...
Large language models (LLMs) can simulate clinical reasoning based on natural language prompts, but their utility in ophthalmology is largely unexpl...
Ophthalmic surgical robots offer superior stability and precision by reducing the natural hand tremors of human surgeons, enabling delicate operatio...
This paper studies the role of attention heads in CLIP's image encoder. While CLIP has exhibited robust performance across diverse applications, we ...
As the appearance of medical images is influenced by multiple underlying factors, generative models require rich attribute information beyond labels...
Online surgical phase recognition has drawn great attention most recently due to its potential downstream applications closely related to human life...