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Alzheimer's disease (AD) pathology begins years before symptoms emerge, making early detection essential. Eye tracking offers a rapid, non-invasive means of identifying early cognitive decline through oculomotor disturbances. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)- and Population, Intervention, Comparison, and Outcome (PICO)-guided systematic review evaluated studies from PubMed, ACM Digital Library, and Google Scholar on eye tracking in mild cognitive impairment (MCI), AD, and related dementias. Seventy-one studies met the inclusion criteria. Antisaccade tasks consistently distinguished AD and MCI from healthy controls, with impaired accuracy, longer latencies, and reduced gain. Non-saccadic paradigms (e.g., visual search, free viewing) indicated diminished exploratory behavior in AD, with mixed findings in MCI. A major limitation was the lack of cohorts defined by current biological criteria, hindering clinical translation. In a subset, classical machine-learning (ML) models and deep neural networks reported accuracies of 0.72 to 0.97. Overall, antisaccade tasks show strong promise for early AD screening; future work should adopt biologically defined cohorts and scalable, accessible eye-tracking technologies. Highlights: Antisaccade tasks best distinguish AD and MCI from HCs.Visual search/free-view tasks showed diminished exploratory behavior in AD.Most studies lack biomarker-based AD criteria, creating a major research gap.
Verón et al. (Tue,) studied this question.