Using Explainable AI in the Clinical Validating of MyCog: A Self-administered Cognitive Screener for Primary Care Settings (P1-13.003)

Callie Jones; Stephanie Ruth Young; Greg Byrne; Elizabeth Dworak; Julia Yoshino Benavente; Richard Gershon; Michael Wolf; Cindy Nowinski

doi:10.1212/wnl.0000000000213226

June 9, 2026Neurology

Using Explainable AI in the Clinical Validating of MyCog: A Self-administered Cognitive Screener for Primary Care Settings (P1-13.003)

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Key Points

The study aims to evaluate the efficacy of MyCog in identifying cognitive impairment using machine learning techniques.
Utilized machine learning algorithms with explainable AI techniques.
Assessed discrimination accuracy of MyCog among older adults.
Focused on enhancing clinical interpretability of the screening outcomes.
MyCog demonstrated significant accuracy in distinguishing cognitively impaired older adults from those without impairment.
Explainable AI methods improved interpretability of results for clinicians.

Implication

Examines the accuracy of MyCog to detect cognitive impairment in older adults, suggesting potential improvements in primary care diagnosis.

Abstract

This study examined whether MyCog, a brief tablet-based cognitive screening application, could accurately discriminate between older adults with and without cognitive impairment using machine learning with explainable AI (XAI) methods to enhance clinical interpretability.

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Cite This Study

Jones et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf3d6faef96ed7f05647c https://doi.org/10.1212/wnl.0000000000213226

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Using Explainable AI in the Clinical Validating of MyCog: A Self-administered Cognitive Screener for Primary Care Settings (P1-13.003)

Key Points

Implication

Abstract

Discussion

Cite This Study

Using Explainable AI in the Clinical Validating of MyCog: A Self-administered Cognitive Screener for Primary Care Settings (P1-13.003)

Key Points

Implication

Abstract

Discussion

Cite This Study

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