[Ieee_vis] BioVis meeup, “Building Explainable AI Tools for Biomedical Applications“, Grace Guo, Harvard University

[Helena Jambor]

Invitation to next week BioVis meetup:

BioVis meeup, “Building Explainable AI Tools for Biomedical Applications“,
Grace Guo, Harvard University
Scheduled: 18. Feb 2026 at 17:00 to 18:00, CET
Location: Join Zoom Meeting
https://tu-dresden.zoom-x.de/j/67863904836?pwd=mVqoyB6DlxAbFAtN7kx0Ka62KHFmUJ.1

Meeting ID: 678 6390 4836
Passcode: BioV!s2025


Abstract
Advances in deep learning have yielded powerful models for diagnosis and prognosis, yet their “black-box” nature continues to stall clinical adoption—especially in biomedicine, where mistakes have life-changing consequences. Many prior studies have demonstrated how visualizations can be used to provide powerful explanations of generic AI models by translating abstract model mechanics into concrete, perceptual cues. However, biomedical data presents two key hurdles for explainable AI (XAI). Firstly, it spans diverse modalities: static and dynamic images, multi-channel time-series, genomic sequences, and free-text—all of which must be reconciled in a single, coherent explanation. Secondly, any explanation must map onto domain concepts that matter to clinicians; generic heat-maps rarely suffice and can even mislead when they are not contextualized in terms of medical expertise or domain knowledge. In this talk, I will present some of my recent work on domain-centered XAI tools for domain experts such as researchers, doctors, and clinicians. I will also discuss some of the key takeaways from these studies, as well as promising directions of future research leveraging visualizations for biomedical XAI.

_________________________
Dr Helena JAMBOR

National Center for Tumor Diseases, University Cancer Center, NCT-UCC

Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden
Fetscherstraße 74, 01307 Dresden



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