Artificial intelligence-driven eye tracker models for Alzheimer's disease diagnosis: A systematic review and meta-analysis

Background Diagnosis of Alzheimer's disease (AD) is crucial for effective intervention and care planning. Recently, artificial intelligence-driven eye-tracking (AI-driven ET) tools have emerged as promising diagnostic aids. Objective To evaluate the diagnostic accuracy of AI-driven ET models for AD detection. Methods A systematic review and meta-analysis were conducted according to PRISMA2020. Different database and grey literature were searched up to March 2025. Data were analyzed with Meta-Disc 1.4 and R software. This meta-analysis has been registered in PROSPERO (CRD420251020284). Results Ten papers were included in the narrative synthesis and eight in the meta-analysis. Our systematic review found that most studies reported moderate to good accuracy of AI-driven ET tools in AD detection. The meta-analysis revealed that AI-driven ET tools achieved a sensitivity of 0.75 [95% CI: 0.67; 0.79], specificity of 0.75 [95% CI: 0.67; 0.81], positive likelihood ratio of 3.29 [95% CI: 2.36; 4.59], negative likelihood ratio of 0.36 [95% CI: 0.27; 0.48], diagnostic odds ratio of 10.40 [95% CI: 5.58; 19.39], and area under the ROC curve of 0.81. Deep learning seems to have better performance than supervised machine learning (SML). Among classification algorithms, support vector machines appear most robust across studies. The meta-regression identified population size, patient preparation, measurement systems, AI techniques, and SML algorithms as significant sources of heterogeneity. Conclusions AI-driven ET tools suggest moderate to good diagnostic accuracy for distinguishing AD patients from healthy controls, based on available case-control studies. However, evidence for effective screening in broader populations is lacking. Further research is needed to confirm these results across diverse clinical settings and strengthen model robustness.