Abstract Background There is a need for a reliable, easy‑to‑use, widely available, and validated tool for timely cognitive impairment identification. We created a computerized cognitive screening tool (Santé‑Cerveau digital tool (SCD‑ T)) including validated questionnaires and the following neuropsychological tests: 5 Word Test (5‑WT) for episodic memory, Trail Making Test (TMT) for executive functions, and a number coding test (NCT) adapted from the Digit Symbol Substitution Test for global intellectual efficiency. This study aimed to evaluate the performance of SCD‑T to identify cognitive deficit and to determine its usability. Methods Three groups were constituted including 65 elderly Controls, 64 patients with neurodegenerative dis‑ eases (NDG): 50 AD and 14 non‑AD, and 20 post‑COVID‑19 patients. The minimum MMSE score for inclusion was 20. Association between computerized SCD‑T cognitive tests and their standard equivalent was assessed using Pearson’s correlation coefficients. Two algorithms (a simple clinician‑guided algorithm involving the 5‑WT and the NCT; and a machine learning classifier based on 8 scores from the SCD‑T tests extracted from a multiple logistic regression model, and data from the SCD‑T questionnaires) were evaluated. The acceptability of SCD‑T was investigated through a ques‑ tionnaire and scale. Results AD and non‑AD participants were older (mean ± standard deviation (SD): 72.61 ± 6.79 vs 69.91 ± 4.86 years old, p = 0.011) and had a lower MMSE score (Mean difference estimate ± standard error: 1.74 ± 0.14, p < 0.001) than Controls; post‑COVID‑19 patients were younger than Controls (mean ± SD: 45.07 ± 11.36 years old, p < 0.001). All the computerized SCD‑T cognitive tests were significantly associated with their reference version. In the pooled Controls and NDG group, the correlation coefficient was 0.84 for verbal memory, ‑0.60 for executive functions, and 0.72 for global intellectual efficiency. The clinician‑guided algorithm demonstrated 94.4% ± 3.8% sensitivity and 80.5% ± 8.7% specificity, and the machine learning classifier 96.8% ± 3.9% sensitivity and 90.7% ± 5.8% specificity. The acceptability of SCD‑T was good to excellent. Conclusions We demonstrate the high accuracy of SCD‑T in screening cognitive disorders and its good acceptance even in individuals with prodromal and mild dementia stages. SCD‑T would be useful in primary care to faster refer subjects with significant cognitive impairment (and limit unnecessary referrals) to specialized consultation, improve the AD care pathway and the pre‑screening in clinical trials.