Krakow Conference on Computational Medicine 2025

Krakow Conference on Computational Medicine 2025

Enhancing Virtual Human Twin with AI solutions

October 15–17, 2025, Krakow

Organisers

Scope and topics

The conference’s motto is “Enhancing Virtual Human Twin with AI solutions“. Personalized medicine, focusing on the development of in-silico methods replacing in-vivo and in-vitro methods, should more effectively use the solutions brought by the AI ​​revolution based on machine learning and data analysis methods; perceived not as competitive, but as supporting existing modeling and simulation methods.

Given the Organisers’ expertise in both computer simulation and artificial intelligence, the Conference will be an excellent opportunity to gain greater interaction between the communities working in these two fields. Computer technologies and high-performance computing are of key importance for progress in computational medicine and therefore an additional advantage of the Conference will be the inclusion of technical aspects of the use of new computing infrastructures.

The organisationof the Conference is the result of the experience gained by the Sano team during Sano Science Day (2023, 2024) and cooperation in the Life Science Open Space organisation (since 2019) and as well as on a very broad experience of the Faculty of Computer Science AGH and Academic Computer Centre Cyfronet AGH in this area.

Conference topics include

  • Mathematical medical models
  • Multiscale modelling
  • Computational modelling of organs and diseases
  • Patient data management and processing
  • Methods of acquisition, storage and retrieval of information in medicine
  • Analysis of medical images
  • Machine learning models for healthcare
  • Computer simulations using advanced computing infrastructures
  • Surgical planning tools
  • Model and simulation reproducibility and credibility
  • Clinical decision support systems based on artificial intelligence
  • Towards the Virtual Human Twin platform
  • Ethical, legal, and social issues in VHT

Important Dates

  • Instructions for authors  -  10 June  
  • Call for abstracts  – 10 June  
  • Deadline for abstracts  – 21 August 
  • Start of registration – 11 August 
  • Acceptance of abstracts   - 2 September 
  • Deadline for registration of authors  – 10 September   
  • Registration closed – 30 September 
  • Conference – 16 – 17 October 
  • Tutorials – 15 October 

Keynote Lectures

Conference schedule

Thursday, 16 October 2025  
  • 08.30-09.00 – registration
  • 09.00-09.15 – Opening
  • 09.15-10.00 – Keynote 1
  • 10.00-10.50 – Short presentations of posters (20 posters, 2 min/poster)
  • 10.50-11.20 – Coffee break and posters
  • 11.20-12.05 – Keynote 2
  • 12.05-13.20 – 5 contributed talks
  • 13.20-14.00 – Lunch and posters
  • 14.00-14.45 – Keynote 3
  • 14.45-16.00 – 5 contributed talks
  • 16.00-16.20 – Coffee break and posters
  • 16.20-17.05 – Keynote 4
  • 17.05-18.20 – 5 contributed talks

19.30-22.00 – Dinner

Friday, 17 October 2025
  • 08.30-09.00 – registration
  • 09.00-09.45 – Keynote 5
  • 09.45-11.00 – 5 contributed talks
  • 11.00-11.20 – Coffee break and posters
  • 11.20-12.05 – Keynote 6
  • 12.05-13.20 – 5 contributed talks
  • 13.20-14.00 – Lunch and posters
  • 14.00-14.45 – Keynote 7
  • 14.45-16.00 – 5 contributed talks
  • 16.00-16.20 – Coffee break and posters
  • 16.20-17.05 – Keynote 8
  • 17.05-17.30 – Summing up

Tutorials

Organized by the Sano teams on 15 from 15.30 to 18.30.

The Conference will be accompanied by tutorials in the field of computational medicine, AI methods and large-scale computing  on 15 October and on 18 October 2025 for medical doctors and researchers. Active participation in the tutorial will be confirmed with an appropriate certificate.

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Wioletta Niwińska, Anna Kajda-Twardowska, Michał Kosobudzki – Legal and Data Protection Team

The implementation of projects, particularly involving AI solutions, imposes the need for lawyers to exercise due diligence to safeguard the interests of companies, software owners, but also to ensure the safety of end users. Looking at AI projects from a broader perspective is key to securing and developing AI systems that comply with normative principles, rules and standards of trustworthiness.  

The implementation of AI tools requires multidisciplinary teams, which is possible for large entrepreneurs and difficult to achieve for smaller entities such as startups. Compliance with trustworthy AI and responsible AI principles can determine the success of the entire project and is key to building public trust for AI technologies.  

The aim of the workshop is to respond to the needs of lawyers and those working with AI systems who may encounter difficulties related to the legal regulation of advanced solutions in their daily work. The workshop is intended to lead to an increased awareness among participants of the complexity of legal regulations that need to be considered when developing AI solutions. Through this workshop, participants will gain tips on the information needed to verify and the steps worth planning when creating AI solutions. During the workshop, speakers will share their experience in the legal security of the project being developed with a particular focus on IP and data protection issues. This workshop will be a space to share good practices from different regions of the globe. We hope that this workshop will inspire participants to plan projects outside the box and to look for unusual and creative solutions. IP and data issues are crucial elements of any AI software. It is hoped that the universality of the topic will enable the knowledge gained to be applied to most AI and Law projects and contribute to broad interest of the workshop by the audience. The workshop will provide an opportunity for participants to share their experiences and will be a platform for knowledge exchange, as well as an opportunity for networking. The value for the participants will be to broaden their horizons in terms of their roles and assigned responsibilities, which will allow them to more easily understand the perspective of the other people and entities acting within the project. 

The target group of the workshop is the conference participants, regardless of their legal background, who may have different roles in the implementation of the AI project. The participation of representatives from different groups will allow the identification of legal risks from their perspective but may also lead to the development of a common way to mitigate them. The workshop is addressed both to people from the legal world (law students, lawyers) and the broader business world, including developers, C-suite, board of directors, compliance and data protection specialists, engineers and AI architects and people who do not have any experience in implementing AI tools such or those who plan to retrain into the world of artificial intelligence specialization. The workshop is scheduled for half a day. The training will be divided into 2 parts. 

Brain–Gut Axis and How to Study It – Computational Neuroscience Team
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Brain–Gut Axis and How to Study It

Computational Neuroscience Team,

Sano – Centre for Computational Medicine

Authors: Jan K. Argasiński, Cemal Koba, Rosmary Blanco, Monika Pytlarz

j.argasinski, c.koba, r.blanco, m.pytlarz@sanoscience.org

Keywords: Computational neuroscience, brain-gut axis, methodology, study design

Maximum number of participants: 10

Location: Sano – Centre for Computational Medicine, Computational Neuroscience Lab (Czarnowiejska 36, building C5, 3rd floor, 30-054 Kraków)

Introduction

The brain–gut axis, a bidirectional communication network linking the central nervous system and the gastrointestinal tract, is emerging as a interesting domain in understanding neurological, metabolic, and psychiatric disorders. Despite growing biological and clinical interest, computational approaches to studying the brain–gut axis remain basic. This tutorial responds to the need for a methodological framework for researchers aiming to explore the brain–gut axis from a computational neuroscience perspective. Focusing on scientific literature as both a source of knowledge and a foundation for study design, the session will introduce participants to modern tools and strategies for initiating research in this field.

Description of the Tutorial

This 3-hour tutorial serves as both a literature deep-dive and a methodology primer. Participants will work in a guided exploration of the latest scientific publications on the brain–gut axis, with a particular focus on computationally relevant research.

The session is divided into three components:

– Literature Exploration and Mapping:

Participants will learn systematic methods for identifying, filtering, and categorizing relevant literature using tools like PubMed, Scopus, and AI-powered search engines (e.g., Semantic Scholar, Connected Papers). Emphasis will be placed on extracting methodological content, modeling approaches, and data sources.

– Scientific Methodology for Study Planning:

We will demonstrate how to analyze existing studies to extract assumptions, computational models, and data modalities (EEG, fMRI, microbiome profiles, behavioral markers). Participants will collaboratively map gaps and research opportunities, developing hypotheses based on the current state of the field.

– Pipeline Prototyping for Brain–Gut Studies:

The final part of the session will be a walkthrough of how to draft a computational research pipeline. This includes defining input data, identifying possible modeling techniques (e.g., neural networks, graph-based models, Bayesian inference), and conceptualizing evaluation strategies.

The goal is to help participants move from reading to research planning.

The format includes short lectures, guided group work, and discussion.

Knowledge and Skills to Be Gained

By the end of the tutorial, participants will be informed on how to:

• Conduct literature reviews focused on computational neuroscience topics.

• Identify and extract methodological insights from scientific publications.

• Recognize and categorize computational approaches applicable to brain–gut research.

• Formulate basic study designs for modeling the brain–gut axis.

• Draft conceptual pipelines for computational experiments, including data selection, modeling strategies, and analysis plans.

• Understand the interdisciplinary landscape of brain–gut research and the role of computational tools within it.

References
    1. Mayer, E. A., Nance, K., & Chen, S. (2022). The gut–brain axis. Annual review of medicine, 73(1), 439-453.
    2. Mayer, E. A., Naliboff, B. D., & Craig, A. B. (2006). Neuroimaging of the brain-gut axis: from basic understanding to treatment of functional GI disorders. Gastroenterology, 131(6), 1925-1942.
    3. Burns, J. L. (2009). The Scientific Method through the Lens of Neuroscience; From Willis to Broad. In Forum on Public Policy Online (Vol. 2009, No. 2). Oxford Round Table. 406 West Florida Avenue, Urbana, IL 61801.
    Acknowledgements

    This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 857533 and from the International Research Agendas Programme of the Foundation for Polish Science No MAB PLUS/2019/13. 

    The tutorial was created within the project of the Minister of Science and Higher Education “Support for the activity of Centers of Excellence established in Poland under Horizon 2020” on the basis of the contract number MEiN/2023/DIR/3796. 

    We gratefully acknowledge Poland’s high-performance Infrastructure PLGrid ACC Cyfronet AGH for providing computer facilities and support within computational grant no. PLG/2025/018289.

      Support for allergy specialists – Computational Intelligence​ Team
      Virtual Reality for Medical Data Visualisation and Interaction – Medical Imaging and Robotics Team
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      Przemysław Korzeniowski1, Kuba Chrobociński1,2, Michał Motak1

      1Sano Centre for Computational Medicine, Czarnowiejska 36, 30-054 Kraków, Poland
      2 Division of Clinical Medicine, School of Medicine & Population Health, University of Sheffield, S1 2TN, Sheffield, United Kingdom
      p.korzeniowski@sanoscience.org, k.chrobocinski@sanoscience.org, m.motak@sanoscience.org

      Keywords: Virtual Reality, Medical Imaging, Visualisation, Data Interaction

      1. Introduction

      While Virtual and Augmented Reality (VR/AR) are widely recognised for their entertainment applications, their utility is rapidly expanding across diverse industries. Beyond popular uses in gaming, flight, and driving simulators, these technologies offer safe and cost-effective platforms for skill development and gaining practical insights. Immersive simulations, in particular, mitigate the risks and expenses associated with traditional training methods.

      The medical field stands out as a particularly promising area for VR. Its applications range from psychology and surgery to comprehensive training programs. These demonstrate not only cost-effectiveness but also the ability to introduce novel elements that significantly enhance user perception and engagement. 

      2. Description of the tutorial

      The tutorial will introduce basic concepts used for the development of interactive environments in Unity Game Engine for medical applications. DICOM images import and interaction are going to be presented. Example Virtual Reality environments will be explored, and their strong sides and limitations are going to be discussed. Surgical training simulators leveraging haptic devices will be presented. 

      3. Knowledge and skills to be gained

      ● Basic Unity Game Engine concepts

      ● Challenges associated with Virtual Reality for medical imaging

      ● Setting up a simple visualisation of a 3D image in VR using Unity Game Engine

      ● Usage of Surgical Simulators with Haptic Feedback

      Maximum number of participants: 10

      Acknowledgements

      This work is supported by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 857533 (Sano) and the International Research Agendas program of the Foundation for Polish Science, co-financed by the European Union under the European Regional Development Fund.

      References

      1. P. Korzeniowski, S. Płotka, R. Brawura-Biskupski-Samaha and A. Sitek, “Virtual Reality Simulator for Fetoscopic Spina Bifida Repair Surgery,” 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 401-406, doi: 10.1109/IROS47612.2022.9981920. keywords: {Training;Pregnancy;Solid modeling;Spinal cord;Computational modeling;Spine;Surgery},

      2. A. D. Kaplan, J. Cruit, M. Endsley, S. M. Beers, B. D. Sawyer, and P. A. Hancock. The effects of virtual reality, augmented reality, and mixed reality as training enhancement methods: A meta-analysis. Human factors, 63(4):706–726, 2021

      3. J. Qian, D. J. McDonough, and Z. Gao. The effectiveness of virtual reality exercise on individual’s physiological, psychological and rehabilitative outcomes: a systematic review. International journal of environmental research and public health, 17(11):4133, 2020

      Verification, Validation and Uncertainty Quantification (VVUQ) – Scientific Programmers Team
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      Scaling up your VVUQ Workflows.
      Practical Automation with EasyVVUQ and Dask on HPC.

      Karol Zając1, Piotr Nowakowski1,2, and Levente Sandor3

      1Sano Centre for Computational Medicine, Czarnowiejska 36, 30-054 Kraków, Poland
      2ACC Cyfronet AGH, ul. Nawojki 11, 30-950 Kraków, Poland

      3Department of Hydrodynamic Systems, Budapest University of Technology and Economics

      Faculty of Mechanical Engineering, Műegyetem rkp. 3, H-1111 Budapest, Hungary
      k.zajac,p.nowakowski@sanoscience.org, lsandor@hds.bme.hu

      Keywords: VVUQ, Sensitivity Analysis, Workflow Automation, Large-Scale, HPC

      1. Introduction

      This tutorial introduces participants to modern tools for Verification, Validation, and Uncertainty Quantification (VVUQ) in scientific modeling, with a focus on automating and scaling VVUQ workflows on High-Performance Computing (HPC) infrastructure. Through the use of the EasyVVUQ library and Dask parallel computing framework, researchers can efficiently run complex Sensitivity Analysis (SA) and Uncertainty Quantification (UQ) campaigns across many simulations. The session aims to demonstrate how these tools integrate seamlessly with HPC schedulers such as SLURM and MPI environments, enabling robust, reproducible, and scalable VVUQ pipelines.

      2. Description of the tutorial

      This 1-hour hands-on tutorial blends conceptual introduction with live demonstrations. It is designed for researchers and software engineers looking to adopt or streamline VVUQ methodologies in their computational workflows. The session will cover:

      1. VVUQ Concepts and Motivation: Introduction to VVUQ in scientific computing.

      2. Automating VVUQ with EasyVVUQ: Learn to define parameters, set up encoders/decoders, and run campaigns with EasyVVUQ.

      3. Scalable Execution with Dask: Use Dask (JobQueue/MPI) with SLURM to distribute jobs and monitor performance.

      4. Use Case: Hemodynamics Simulation Campaign (VirtualFD): Hands-on application to a real-world example from GEMINI project.

      3. Knowledge and skills to be gained

      By the end of this tutorial, participants will be able to:

      • Understand the principles of VVUQ and their role in computational modeling.

      • Set up and manage VVUQ workflows using EasyVVUQ.

      • Configure and run large-scale UQ/SA simulations using EasyVVUQ and Dask.

      • Analyze and visualize sensitivity and uncertainty results efficiently.

      Maximum number of participants: 12, Location: Sano Centre for Computational Medicine

      Acknowledgements: This tutorial is made possible by the contributions and results from the following projects: InSilicoWorld (grant agreement no. 101016503), GEMINI (grant agreement no. 101083771).

      References:

      [1] InSilicoWorld project: https://insilico.world/

      [2] GEMINI project: https://dth-gemini.eu

      [3] Suleimenova, D., Arabnejad, H., Edeling, W., Coster, D., Luk, O., Lakhlili, J., … Groen, D. (2021). Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit. Journal of Computational Science, 53. doi:10.1016/j.jocs.2021.101402

      [4] P.L.J. Hilhorst, B.B.E. van de Wouw, K. Zajac, M. van ’t Veer, P.A.L. Tonino, F.N. van de Vosse and W. Huberts. “Sensitivity analysis for exploring the variability and parameter landscape in virtual patient cohorts of multi-vessel coronary artery disease.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 383, no. 2293. https://doi.org/10.1098/rsta.2024.0230

      [5] EasyVVUQ: Uncertainty intervals for everyone: https://easyvvuq.readthedocs.io/en/dev/

      [6] Dask Distributed documentation web site: https://distributed.dask.org/en/stable/

      Your Journey to HPC and Beyond.
      A Guide to Research at Scale with the Model Execution Environment       – Academic Computer Centre Cyfronet AGH
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      Marek Kasztelnik2, Piotr Nowakowski1,2, and Piotr Połeć2

      1Sano Centre for Computational Medicine, Czarnowiejska 36, 30-054 Kraków, Poland
      2ACC Cyfronet AGH, ul. Nawojki 11, 30-950 Kraków, Poland
      {p.nowakowski}@sanoscience.org, {m.kasztelnik,p.polec}@cyfronet.pl

      Keywords: modelling, simulation, HPC, SLURM, Model Execution Environment

      1. Introduction

      This tutorial provides an introduction to High-Performance Computing (HPC) and its application in scientific research. Participants will be guided through the fundamental concepts of HPC, learning how to leverage powerful computing resources for their research needs. The tutorial will feature the Model Execution Environment (MEE), a platform designed to simplify the execution of complex simulations and data analysis pipelines on HPC infrastructure. We will explore how MEE supports the integration and execution of scientific applications, drawing on real-world examples from European research projects.

      2. Description of the tutorial

      This 2-hour tutorial will be a blend of lectures and live demonstrations. The session will cover the following topics:

      1. Introduction to HPC and the SLURM queuing system: We will begin with an overview of HPC concepts and a practical guide to using the SLURM workload manager for submitting and managing jobs on a cluster.
      2. API-driven job submission: This segment will focus on programmatic job submission. Participants will learn how to use an API to submit jobs to the HPC cluster and how to integrate this functionality with an external web application.
      3. Introduction to the Model Execution Environment (MEE): The final part of the tutorial will introduce the MEE. We will showcase how MEE streamlines the process of running scientific applications. This will involve:
      4. an overview of how to define and manage complex computational workflows.
      5. a case study from the InSilicoWorld project, demonstrating how MEE was used to store cohort data and run hundreds of simulations as part of a large-scale computational campaign.
      3. Knowledge and skills to be gained

      Upon completion of this tutorial, participants will be able to:

      • Understand the fundamentals of High-Performance Computing,
      • Submit and manage computational jobs on an HPC cluster using the SLURM scheduler,
      • Programmatically submit jobs to an HPC cluster via an API,
      • Understand the purpose and benefits of the Model Execution Environment (MEE).

      Maximum number of participants: 20, Location: ACC Cyfronet AGH

      Acknowledgements: This tutorial is made possible by the contributions and results from the following projects: EDITH (grant agreement no. 101083771), InSilicoWorld (grant agreement no. 101016503), GEMINI (grant agreement no. 101083771).

      References:
      1. InSilicoWorld project: https://insilico.world/
      2. EDITH project: https://www.edith-csa.eu
      3. GEMINI project: https://dth-gemini.eu
      4. Sano seminar: Composing Applications In The Model Execution Environment: https://www.youtube.com/watch?v=hFM1jPVAhtg
      5. Kasztelnik, M. et al. (2023). Digital Twin Simulation Development and Execution on HPC Infrastructures. In: Mikyška, J., de Mulatier, C., Paszyński, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_2

      Venue

      Tutorials (15 October 2025) – at the premises of organising institution 

      Conference (16-17 October 2025) – at the premises of the Faculty of Computer Science AGH – Building D17,  Kawiory Street 21, 30-055 Krakow (map) 

      Steering Committee

      • Marian Bubak – Sano Centre
      • Maciej Malawski – Sano Centre
      • Marek Kisiel-Dorohinicki – Faculty of Computer Science AGH
      • Marek Magryś – Academic Computer Centre Cyfronet AGH

      Program Committee

      • Jan K. Argasiński – Sano Centre for Computational Medicine
      • Aleksander Byrski – Faculty of Computer Science AGH
      • Ewa Deelman – University of Southern California, USA
      • Przemysław Korzeniowski – Sano Centre for Computational Medicine
      • Tomasz Kościółek – Sano Centre for Computational Medicine
      • Rafał Niżankowski – Sano Centre for Computational Medicine
      • Andrew Narracott – University of Sheffield Sano UK
      • Wiesław Nowiński – Sano Centre for Computational Medicine
      • Jose Sousa – Sano Centre for Computational Medicine
      • Ewelina Szymańska-Skolimowska – Sano Centre for Computational Medicine
      • Olav Zimmerman – Juelich Supercomputing Center, DE

      Organising Committee

      • Katarzyna Baliga-Nicholson – Sano Centre for Computational Medicine
      • Magdalena Chrzanowska – Sano Centre for Computational Medicine
      • Dominik Czaplicki – Sano Centre for Computational Medicine
      • Tomasz Gubała – Sano Centre for Computational Medicine
      • Marta Jarkiewicz – Sano Centre for Computational Medicine
      • Marta Maj – Academic Computer Centre Cyfronet AGH
      • Katarzyna Niziołek-Jarominek – Wydział Informatyki AGH
      • Anna Partyka – Sano Centre for Computational Medicine
      • Maria Sendecka – Sano Centre for Computational Medicine 

      Call for contributions – instructions for authors

      Contributions will be accepted based on the assessment by the Program Committee of two-page abstracts (approx. 2300 characters) with a fixed structure:

      • Introduction
      • Description of the problem
      • Related work
      • Solution of the problem
      • Conclusions and future work
      • References: Submitted abstracts will be evaluated for compliance with the KCCM topics, originality, novelty, technical accuracy of the description, completeness of sections, readability, compliance with the KCCM framework, and selection of references.

      Please follow the template of the abstract.

      Registration and fee

      No registration fee, conference by invitations

      Watch the key highlights from last year’s Sano Science Day, where our scientists, engineers, and experts shared their achievements and innovations.