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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...

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Updated: Jul 16, 2026

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss
07:12

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

Published on: April 11, 2025

Medical Vision-Language Models: Existing Technologies, Clinical Applications and Future Directions.

Le Zou1, Mengyu Ma1, Jun Li1

  • 1College of Electronic Science and Technology, National University of Defense Technology, No. 109 Deya Road, Kaifu District, Changsha 410073, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

Vision-Language Models (VLMs) are revolutionizing medical image analysis by integrating visual data with clinical text. This review synthesizes 167 studies, outlining VLM principles, applications, and challenges for trustworthy clinical diagnostics.

Keywords:
artificial intelligenceclinical applicationmedical image analysismulti-modal learningvision-language model

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Last Updated: Jul 16, 2026

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss
07:12

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

Published on: April 11, 2025

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • Conventional single-modal deep learning faces challenges in medical image analysis due to data variability and sensor constraints.
  • Vision-Language Models (VLMs) offer a new approach by bridging the gap between medical images and clinical narratives.

Purpose of the Study:

  • To systematically review the technological evolution and clinical utility of VLMs in medical image analysis.
  • To provide a roadmap for VLM development and application in healthcare.

Main Methods:

  • Systematic literature synthesis following PRISMA guidelines, analyzing 167 representative studies.
  • Distillation of VLM mechanisms into seven core operational principles.
  • Quantitative cross-comparison of benchmark VLM architectures.

Main Results:

  • VLMs demonstrate potential in few-shot diagnosis, prompt-driven segmentation, and multi-task foundation models.
  • Identified clinical bottlenecks include segmentation failures, diagnostic hallucinations in rare diseases, and computational complexity.
  • Proposed a framework for transitioning to dynamic, multi-source sensor-driven intelligence.

Conclusions:

  • VLMs are transforming medical image analysis, but challenges remain for widespread clinical adoption.
  • Future directions focus on developing sensor-aware, trustworthy clinical diagnostic agents by addressing physical and algorithmic limitations.