Beyond the Clinic: Rethinking Cognitive Screening at Scale Using AI:
As the global population ages, the urgency of detecting cognitive decline early grows. Yet standard tools—like the MMSE, MoCA, and Clock Drawing Test (CDT)—were designed for clinics, not the daily lives of the millions who need monitoring most. My research explores the question: what would cognitive assessment look like if designed from scratch for the ubiquitous computing era? And can AI help us achieve that goal?
My first project evaluated three frontier AI models on the CDT. While models achieved 94% dementia screening accuracy, granular scoring accuracy dropped to 55%. Notably, when all three models agreed, they were collectively wrong 22% of the time, revealing shared architectural blind spots. This suggests that clinical metrics are fundamentally mismatched with how these AI systems behave.
Building on this, I am designing "cognitive microtasks"—brief, drawing-based exercises featuring everyday objects with strategically missing features. These are built for frequent, self-administered, and practice-effect-free monitoring. Crucially, each task is paired with a structured rubric that decomposes drawing quality into domain-specific questions, enabling reliable AI scoring. Additionally, vision-language models can generate unlimited new stimuli on demand.
Together, these projects reflect a unified vision: a future where cognitive health is monitored continuously and unobtrusively in everyday life, powered by assessments designed from the ground up for both human completion and AI evaluation.