Artificial intelligence & neuroscience

Selected publications

Recent research on machine learning and digital biomarkers in neurodegenerative diseases.

Machine learning and digital biomarkers can detect early stages of neurodegenerative diseases

This paper shows that explainable machine learning methods, combined with digital biomarkers such as eye tracking and online cognitive tests, can help identify early signs of Parkinson's and Alzheimer's disease before clear clinical symptoms appear.

Machine learning recognizes stages of Parkinson's disease using magnetic resonance imaging

This paper applies interpretable machine learning techniques to structural MRI data to distinguish between control, prodromal, and Parkinson's disease groups, supporting research on early diagnosis.

Machine learning methods for Parkinson's disease datasets

This book chapter describes explainable machine learning workflows using DTI imaging, eye tracking, and online cognitive testing to study Parkinson's disease progression and explore early screening approaches.

About

Dr. Artur Chudzik is an AI engineer and researcher specializing in medical artificial intelligence and computational neuroscience. His research focuses on explainable machine learning methods for the early detection and monitoring of neurodegenerative diseases, including Parkinson's and Alzheimer's disease.

He holds a PhD in Computer Science from the Polish-Japanese Academy of Information Technology in Warsaw and earned both his M.Sc. and B.Sc. degrees in Computer Engineering from Rzeszow University of Technology.

His research explores digital biomarkers derived from brain imaging, online cognitive testing, eye-tracking, and movement analysis. By combining interpretable machine learning with accessible digital tools, his work aims to enable earlier and more reliable detection of neurological disorders.

Alongside academic research, he has more than a decade of experience in software engineering and applied AI, building intelligent systems that are transparent, reliable, and clinically useful.

Research interests

Artificial intelligence

I work on building machine learning systems for biomedical data that are not only accurate, but also understandable. I am especially interested in explainable AI - models that clinicians can trust and interpret. When AI is transparent, it can reveal meaningful patterns in data and make it easier to bring these tools into real healthcare settings.

Neuroscience

My research explores how computational models can help us better understand the brain, particularly in neurodegenerative diseases. By connecting data analysis with biological processes, I aim to identify early changes in the brain and contribute to earlier detection and intervention strategies.

Complex systems

I am fascinated by complex systems - from high-dimensional data to mathematical models that describe how patterns emerge. Using tools from applied mathematics and data science, I study how structure and behavior arise in complicated systems, with the goal of improving both prediction and understanding.