Digital Biomarkers May Support Alzheimer Disease Screening in Primary Care
KEY TAKEAWAYS
- Digital biomarker tools may help detect early Alzheimer disease–related cognitive, motor, language, and spatial-navigation changes in primary care settings.
- Reviewed technologies included eye tracking, speech analysis, handwriting/gesture analysis, virtual reality navigation, and gait/turning measures.
- Standardized thresholds, platform consistency, and longitudinal validation are still needed before broad clinical implementation.
Digital biomarker tools may help identify early Alzheimer disease (AD)–related changes in primary care settings, according to findings from a narrative review presented at the 2026 Alzheimer’s Association International Conference (AAIC).
The study investigators reviewed literature published from 2020 to 2026 on digital biomarkers, primary care screening, and digital phenotyping. Of 22 records initially screened, 7 peer-reviewed studies met inclusion criteria and were assessed for diagnostic performance, scalability, and ability to capture neuroanatomical or functional changes without specialized neurologic hardware.
Digital Biomarker Findings
- Eye-tracking approaches differentiated cognitive impairment and, in reviewed studies, outperformed plasma biomarkers.
- Speech-based natural language processing models detected subtle language changes, including altered pronoun use and pauses.
- Handwriting and hand-gesture analyses captured motor and dexterity changes, with artificial intelligence (AI)-based gesture recognition reaching 90% accuracy in tracking dementia severity.
- Virtual reality spatial navigation and dual-task turn velocity measures identified MCI or dementia and were associated with hippocampal atrophy.
Source
Alba Gonzalez M, García Vázquez MS, Babatope EY. Digital biomarkers for Alzheimer’s disease screening in primary care: a narrative review. Poster presented at the Alzheimer's Association International Conference (AAIC); July 12–15, 2026; London, United Kingdom, and online.