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
Workshop Schedule
TBA
Invited Talk
TBA
Important Dates
- Paper submission deadline: 28.02.2026
- Notification of acceptance: 13.03.2026
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. 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 format given on the AIRoV homepage.
Submission link: https://cmt3.research.microsoft.com/AIROV2026/