Eye‐tracking technology is an innovative tool that holds promise for enhanced dementia screening, offering the potential of brief and quantitative assessment of cognitive functions. Critically, instruction‐less eye‐tracking tests may ameliorate some of the issues with complex test instructions and linguistic variations associated with traditional cognitive tests, and capture additional sensitive metrics of task performance. However, the extraction of relevant biomarkers from large, complex eye‐tracking datasets is non‐trivial. In this work, we introduce a novel automated way of extracting abnormal oculomotor biomarkers using machine learning from raw eye‐tracking data acquired during an instruction‐less cognitive test.A free‐viewing instruction‐less cognitive battery (5 minutes) was administered to healthy controls (N=553) and patients with a range of dementias (N = 30) [Figure 1]. Our method is …