Artur Chudzik is an AI engineer and researcher building explainable AI for earlier detection
of Parkinson's and Alzheimer's disease. His work turns subtle patterns in cognition, gait,
facial expression, eye movements, and brain imaging into interpretable digital biomarkers for
monitoring disease and guiding treatment decisions. He holds a PhD in Computer Science from
the Polish-Japanese Academy of Information Technology and is a member of the Digital Biomarkers
in Neurodegenerative Diseases research group. He also has more than a decade of experience in
software engineering and applied AI, which helps him turn research ideas into reliable,
transparent, and practical systems.
My work asks a simple question: can we detect neurodegenerative disease earlier
by learning from subtle patterns in behavior, cognition, and the brain?
Neurodegenerative diseases are brain diseases in which nerve cells gradually lose
function and die. Timing matters because visible motor symptoms in Parkinson's disease often appear
only after many dopamine-producing neurons in the substantia nigra are already dead.
In Alzheimer's disease, damaging brain changes can begin decades before dementia
is diagnosed. The disease process can be underway while the
evidence still looks incomplete, ambiguous, or too late to act on confidently.
Earlier signals can help clinicians monitor risk, choose interventions, and adjust treatment
before the disease trajectory becomes harder to change.
The methodological challenge is not only to classify disease, but to make the evidence
behind a prediction understandable. That is why this work combines brain imaging, online
cognitive testing, eye tracking, movement analysis, facial expression analysis, and other
digital biomarkers with interpretable machine-learning methods.
