Workshop Description
AI is revolutionizing diagnostic and therapeutic practices, driving significant innovations across the entire medical domain, from disease identification and early intervention to surgical procedures and clinical workflows.
The AI in Medicine workshop aims to create a forum that brings together interdisciplinary groups to present and discuss novel contributions, works in progress, emerging trends, and future challenges of AI applications in healthcare. Our goal is to strengthen the local network, foster collaboration and provide a supportive environment for students and early-career researchers.
Submissions are encouraged across a wide range of topics including, but not limited to:
- Surgical Data Science
- Generative and Foundation Models in Medicine
- Multi-modal Learning and Data Fusion
- Physics-based Modeling with Deep Learning
- Pattern Recognition and Time-series Analysis
- Computer-aided Diagnosis
- Image Classification, Segmentation, and Anomaly Detection
- Image Registration, Reconstruction, and Synthesis
- Computational Pathology and Digital Histology
- AI-driven Clinical Imaging Studies
- Multi-modal Medical Image Retrieval
- Real-time AI in Medicine and Surgery
- AR/VR for Medical Intervention and Education
- NLP for Clinical Documentation and Data Extraction
- Predictive Analytics for Patient Outcomes
- AI in Drug Discovery and Development
- Personalized Medicine and Treatment Planning
- AI Integration into Clinical Workflows
Invited Talk
Spiros Denaxas, IT:U, UCL
Using data and AI to improve human health and healthcare
Spiros is Professor of Computational Medicine at IT:U and Professor of Biomedical Informatics at University College London in the UK. He is a Visiting Professor at the University of Athens Medical School where he teaches health data science for electronic health records research and Associate Director and the British Heart Foundation Data Science Centre where he leads the Defining Disease theme. A computer scientist by background, his research focuses on developing computational methods for analyzing complex biomedical datasets in order to improve our understanding of disease mechanisms.
Workshop Schedule on Tuesday 14 April, 2026
The following table* reflects selected oral presentations for the AIM workshop.
| Time | Presentation Title | Speaker |
|---|---|---|
| 13:30 - 13:35 | Opening remarks | Track chairs |
| 13:35 - 14:15 | Using data and AI to improve human health and healthcare | Spiros Denaxas (IT:U Linz, UCL) |
| 14:15 - 14:30 | Exploiting Intermediate Reconstructions in Optical Coherence Tomography for Test-Time Adaption of Medical Image Segmentation | Thomas Pinetz (Medical University of Vienna), Veit Hucke (Medical University of Vienna), Hrvoje Bogunovic (Medical University of Vienna) |
| 14:30 - 14:45 | Quantifying and Mitigating Sycophantic Bias in Medical Diagnostic LLM | Nathanya Satriani (Carinthia University of Applied Sciences) |
| 14:45 - 15:00 | MotionDPS: Motion-Compensated 3D MRI Reconstruction | Antonio Ortiz Gonzalez (University of Bonn), Erich Kobler (Johannes Kepler University Linz), Lukas Schletter (German Center for Neurodegenerative Diseases), Alexander Effland (University of Bonn) |
| 15:00 - 15:30 | Coffee Break | |
| 15:30 - 15:45 | Flow Matching for Conditional MRI-CT and CBCT-CT Image Synthesis | Arnela Hadzic (Medical University of Graz), Simon Johannes Joham (Medical University of Graz), Martin Urschler (Medical University of Graz) |
| 15:45 - 16:00 | CavitOmiX: A Proteome-Wide AI Framework for Structure-Based Off-Target Prediction, Drug Repurposing, and Antiviral Discovery | Michael Hetmann (Innophore) |
| 16:00 - 16:15 | xLSTM for Irregular Multivariate Clinical Time-Series Forecasting | Laura Legat (Johannes Kepler University Linz), Erich Kobler (Johannes Kepler University Linz) |
| 16:15 - 16:30 | Evaluation of Anatomical Shape Priors in Deep Learning-Based Cardiac Multi-Compartment Segmentation | Michael Hudler (Medical University Graz), Franz Thaler (Medical University Graz), Martin Urschler (Medical University Graz) |
| 16:30 - 16:45 | Evidential Deep Learning for Missing Boundary Detection in Topologically Constrained OCT Layer Segmentation | Botond Fazekas (Medical University of Vienna), Hrvoje Bogunovic (Medical University of Vienna) |
| 16:45 - 16:55 | Closing remarks | Track chairs |
* schedule slots might be subject to rearrangement
Important Dates
- Paper submission deadline: 14.03.2026
- Notification of acceptance: 20.03.2026
- Camera-ready deadline: 03.04.2026
Submission
The workshop invites contributions in the form of extended abstract papers (2-4 pages) presenting early ideas, works in progress, and recently published journal work. Submitted papers will follow a double-blind review process. Accepted papers will be presented as posters and may be selected for oral presentation.
Extended abstracts should be submitted via Microsoft CMT
(please select the 'AI in Medicine' track) following the AIRoV paper format.
Paper format: AIRoV 2026 paper format
Submission link: https://cmt3.research.microsoft.com/AIROV2026/