Reference

Purpose

Method

Outcome

Gap

[9]

Assess gamified approach for visual acuity in children

Mobile game- based assessment

Improved engagement and potential accuracy in pediatric visionassessment

Limited specificity to particular disorders

[19]

Measure Contrast Sensitivity Function (CSF)in amblyopic children

Game-based CSF test

Differentiation between healthy and amblyopiceyes

Potential limitations of game format

[20]

Evaluate self- administered contrast sensitivity tests inchildren

Gamified iPad app

Enhanced accessibility to vision screening

Variability in device and environmentusage

[23]

Identify dyslexia througheye-tracking analysis

Eye-trackingtechnology

Objective dyslexiascreening method

Validation acrossdiverse populations

[12]

Utilize eye-tracking and machine learning fordyslexia screening

Machine learning models on eye-tracking data

High accuracy in dyslexia detection

Standardization across differentenvironments

[24]

Investigate Beynex app for cognitive decline screening

Gamified cognitive tests

Moderate correlation with MoCA ratings

User demographic diversity and clinicalvalidation

[3]

Explore XR for visionfunction testing

XR
technologyapplications

Enhanced engagementand accessibility

Standardization andclinical integration

[22]

Develop gamified onlinetest with machine-learning for dyslexia screening

Online gamifiedtest with ML model

Achieved over 80%accuracy in dyslexia detection

Validation acrossdifferent datasets and environments