Abstract
Mild cognitive impairment (MCI) is a critical transitional stage between normal aging and dementia that remains challenging to detect. Many traditional neuropsychological assessments designed for early detection of MCI are time-consuming, require specialized training and/or demonstrate limited validity, making them impractical for widespread use in primary care settings. This article is divided into two phases. The first phase provides a rapid review of the current landscape of cognitive screening tools, while the second presents a novel, fully automated, digital screener based on two tasks from the Creyos cognitive assessment platform. This novel screener has been designed, using machine learning, for rapid administration without clinical supervision. The preliminary findings demonstrate that our two-task screener effectively differentiates between cognitively normal individuals and those at risk of progression along the Alzheimer's disease continuum. Furthermore, validation analyses showed that the screener has high sensitivity and specificity, outperforming many conventional assessments. By offering a brief, accessible, and reliable alternative to standard screening tools, our screener has the potential to enhance early detection efforts and facilitate timely intervention, ultimately improving patient outcomes, reducing the burden on clinicians, and optimizing healthcare resources.