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Updated: Jan 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Is the MMSE enough for MCI? A narrative review of the usefulness of the MMSE
Giorgia D'Ignazio1, Leonardo Carlucci2, Maria Rita Sergi1
1Department of Psychology, University of G.d'Annunzio, Chieti-Pescara, Chieti, Italy.
Background:
Mild Cognitive Impairment (MCI) is a clinical condition characterized by a decline in cognitive functions greater than expected for an individual's age and educational level, yet not severe enough to significantly interfere with daily activities. Early identification of MCI is crucial for targeted interventions, monitoring symptom progression, and implementing appropriate therapeutic or support strategies. In clinical practice, screening tools are often used that show clear limitations in sensitivity and in their ability to detect mild cognitive alterations.
Methods:
Although the Mini-Mental State Examination (MMSE) is among the most widely used tools for initial assessment in non-specialist settings, it presents significant psychometric limitations in identifying MCI. A critical analysis of its effectiveness is therefore essential to guide an informed choice of assessment instruments.
Results:
A narrative review of the literature suggests that the MMSE shows reduced sensitivity in detecting MCI, with a tendency to underestimate mild cognitive deficits. Specific cognitive domains are insufficiently captured by the MMSE compared to more sensitive tools.
Conclusion:
In light of these limitations, this review highlights the need to adopt alternative cognitive tests for the early detection of cognitive impairment.
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