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Dagstuhl Seminar 23451

Visualization of Biomedical Data – Shaping the Future and Building Bridges

( Nov 05 – Nov 10, 2023 )

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The last decades of advancements in biology and medicine and their interplay with the visualization domain proved that these fields are naturally tightly connected. Visualization plays an irreplaceable role in making, understanding, and communicating biological and medical discoveries.

This Dagstuhl Seminar will serve as an interdisciplinary platform for a collective approach to the contemporary and emerging future scientific and societal challenges at the intersection of visualization, biology, and medicine in the context of increasing complexity in data, data analytics and data-intensive science communication. Building on the success of the previous seminars and our ongoing community efforts, participants of this seminar will critically tackle three pressing, highly relevant scientific questions of interest to the bioinformatics, medical informatics, and visualization communities:

  1. Mastering the increasing complexity and amount of data that are produced in biomedical research. The situation urgently needs a systematic approach to bridge scales in data, data modalities, and domains. We will address open questions related to the integration of data from different sources and modalities (e.g., imaging, omics, or molecular structures) in holistic visual analytics systems, but also discuss strategies to support the development towards personalized and precision medicine requiring an interdisciplinary approach to fusing data and methods across domains.
  2. The role of visualization in supporting interdisciplinary research and in communicating biological and medical discoveries to experts and broader audiences. Here we want to reflect on and critically evaluate the key role that data visualizations increasingly play in policy-making and shaping public debate, as seen, for instance, during the COVID-19 pandemic. This will be followed by a discussion on establishing visualization as a fundamental bridge between biomedical research and the general public, and the development of a pathway to achieve this vision.
  3. Visualization for a user-centric and trustworthy AI in biomedical applications. Trustworthiness of AI-driven data analytics is one of the core topics of ongoing AI research and part of upcoming legal frameworks in the EU and other countries. Explainability and transparency of machine-made decisions are particularly critical and play a vital role in the biomedical domain due to the prominence of high-risk applications. Central to our discussion within this seminar will be the current use and future potential of visualization in realizing a human-centered and trustworthy AI in the context of biomedical applications catering to a wide diversity of stakeholders.

The primary goal of this Dagstuhl Seminar is to strengthen and widen a sustainable and vibrant interdisciplinary community of biological, medical, and visualization researchers from both academia and visualization through an in-depth, comprehensive, and inclusive exchange of ideas, experiences, and perspectives. Through the identified key topics, spanning methodological, technical, infrastructural, and societal challenges, we plan to seed several discussions and exchange of ideas on the most pressing problems among the biological and biomedical domains and how these problems could be approached through data visualization and open up room for innovation in designs and methodologies. We anticipate that these interactions will result in joint publications, research proposals, and further collaborations among the participants.

Copyright Katja Bühler, Barbora Kozlíková, Michael Krone, and Cagatay Turkay


Related Seminars
  • Dagstuhl Seminar 12372: Biological Data Visualization (2012-09-09 - 2012-09-14) (Details)
  • Dagstuhl Seminar 18161: Visualization of Biological Data - Crossroads (2018-04-15 - 2018-04-20) (Details)
  • Dagstuhl Seminar 21401: Visualization of Biological Data - From Analysis to Communication (2021-10-03 - 2021-10-08) (Details)

  • Graphics
  • Machine Learning
  • Other Computer Science

  • Visualization
  • Interdisciplinary
  • Biology
  • Computational biology
  • Medicine