Bridging AI & Clinical Practice

Developing transparent AI that integrates seamlessly into healthcare

My ambition is to improve patient care by creating robust artificial intelligence that is built with, and for, clinicians. Real-world impact requires bridging scientific innovation with clinical needs.

Explore My Research
Barbora Rehák Bučková

Postdoctoral Researcher

Modelling • AI • Clinical Applications • Multimodal data • Neuroimaging

Radboud University Medical Center

About Me

I'm Barbora Rehák Bučková, my career spans computational biology, AI, and healthcare data, driven by a vision to create AI that doesn't just work, but matters. From my early work at the Czech Institute of Health Information and Statistics to my current research at Radboud University Medical Center, I've focused on developing methodological innovations that translate into real clinical impact.

Latest News & Publications

Research Journey

Current

Postdoctoral Researcher • PRECOGNITION Project

I'm working on structured missingness in clinical data, harmonizing cognitive measures, and creating normative models of cognition to better understand individual differences.

Fulbright Scholar

University of Pennsylvania

My Fulbright Scholarship allowed me to collaborate with Christos Davatzikos at UPenn, where I developed multimodal methodologies for analyzing neuroimaging data.

PhD Studies

AI for Healthcare

During my PhD at Czech Technical University in Prague, I focused on the development of multimodal data analysis methods and development of methods for individualized inferences.

Early Career

Czech Institute of Health Information and Statistics

I analysed and managed clinical studies, evaluated national screening programs, and developed guidelines for implementing new screening programmes across the country.

Bc & Msc Studies

AI for Healthcare

I studied computational biology at Masaryk University in Brno, Czech Republic, where I first discovered my passion for applying computational methods to healthcare challenges.

Research in Progress

1

Structured Missingness in Biomedical Data

In our latest preprint - The Art of Not Knowing: Accommodating Structured Missingness in Biomedical Research, we developed a novel framework to address structured missingness in clinical data - a critical but overlooked challenge where data gaps follow systematic patterns that violate standard imputation assumptions, leading to unreliable conclusions in multi-site studies.

Structured missingness Imputation Data harmonisation
2

Normative models of cognition

As a part of the PRECOGNITION, together with Charlotte Fraza, we are developing populational normative models of cognition to improve individualized inferences in this domain.

Cognition Normative modelling
Veni

Early Warning Systems for Patient Deterioration

Developing AI systems for continuous patient monitoring that integrate seamlessly with clinical workflows, addressing real-world implementation barriers in hospital settings.

Continuous Monitoring Clinical Integration Patient Safety

Works

Let's Connect

Interested in collaboration, mentorship, or discussing AI in healthcare?