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Development and Clinical Validation of an Artificial Intelligence-Based Automated Visual Acuity Testing System.

Kelvin Zhenghao Li1,2, Hnin Hnin Oo1, Kenneth Chee Wei Liang1

  • 1Department of Ophthalmology, National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore.

Life (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study presents an AI-powered automated visual acuity (VA) testing system that accurately assesses vision using speech and image recognition. The system demonstrates feasibility for self-administered, clinic-based eye exams.

Keywords:
artificial intelligenceautomatedvisual acuity test

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Technology

Background:

  • Automated visual acuity (VA) testing systems can enhance clinic efficiency.
  • Integrating artificial intelligence (AI) offers potential for self-administered eye assessments.
  • Current VA testing methods may require significant clinician time and resources.

Purpose of the Study:

  • To develop and validate an AI-driven automated visual acuity (VA) testing system.
  • To enable self-administered, clinic-based VA assessment using speech and image recognition.
  • To evaluate the accuracy, reliability, and user experience of the automated system.

Main Methods:

  • Developed an AI system using Whisper speech recognition and pose estimation.
  • Incorporated a state-driven interface for guided, sequential testing.
  • Validated laboratory performance and compared automated vs. manual VA testing in a clinical setting.

Main Results:

  • AI model significantly reduced word error rates for letter and number recognition.
  • Pose detection accurately identified occluder use.
  • Automated unaided VA showed good agreement with manual testing (ICC=0.77); pinhole VA showed moderate agreement (ICC=0.63).

Conclusions:

  • The AI-based automated VA system is accurate, reliable, and user-friendly.
  • The system demonstrates feasibility for clinical implementation.
  • Automated VA testing shows promise for efficient and accessible vision assessment.