AI and Imaging for Childhood Diseases and Pediatric Cancers
Strasbourg, France · Sept 27 – Oct 1, 2026 · Co-located with MICCAI 2026
Artificial intelligence has the potential to transform care delivery in diverse medical specialties by enabling more advanced characterization of biomedical data. However, applications in pediatrics lag behind due to the rare nature of childhood diseases and disorders, the high variability of pediatric patients due to growth and development, as well as unique clinical workflows relative to adults.
Pediatric diseases present distinct challenges including rapid developmental changes, smaller anatomy, motion artifacts, and the limited availability of large, well-annotated datasets. These considerations complicate tasks such as segmentation, registration, and classification, and undermine direct reuse of adult-trained models.
The PedAItrics workshop focuses on the application of medical imaging and artificial intelligence to pediatric, neo/prenatal, and childhood diseases, where imaging plays a central role in diagnosis and longitudinal management. It will showcase recent advances in AI integrating multimodal data — including imaging, digital pathology, -omics, and EHR data — through deep learning and classical machine learning.
By bringing together clinicians, imaging scientists, and AI researchers, PedAItrics aims to identify shared methodological challenges across pediatric diseases and cancers, promote reproducible and trustworthy AI, and accelerate translation into clinical practice.
We welcome original research, methods, and applications addressing the following themes:
Methods addressing anatomical variability, developmental changes, motion artifacts, and limited cohort sizes in the pediatric domain.
Novel algorithms or adaptations of adult-trained models for pediatric cohorts, including domain adaptation and transfer learning strategies.
Combining imaging, digital pathology, -omics, and clinical EHR data for treatment response assessment, outcome prediction, and precision medicine.
Approaches for small and heterogeneous datasets, longitudinal analysis, and multi-institutional data specific to the pediatric domain.
Translation and commercialization of AI solutions in pediatric care settings, including regulatory considerations and large-scale validation.
Novel strategies for synthetic data generation and augmentation to address data scarcity in pediatric datasets and improve model training.
Overcoming barriers in pediatric research, best practices for cross-institutional collaboration, and technical innovations enabling data sharing.
All deadlines are at 11:59 PM AoE (Anywhere on Earth) unless otherwise stated.
All deadlines are 23:59 Pacific Time.
The workshop will run as a half-day event. The following is a preliminary schedule subject to change.
We are in the process of confirming distinguished keynote speakers from the pediatric AI, imaging, and clinical research communities. Speaker announcements will be made on a rolling basis — check back for updates.
We invite original research contributions addressing AI and medical imaging challenges in pediatric medicine.
Submissions will be handled via OpenReview. The link will be made available when the portal opens.
OpenReview Portal — Coming SoonSubmission deadline: TBD
For inquiries about the workshop, please contact the organizing committee.
Contact OrganizersEmory University / Children's Healthcare of Atlanta
Cincinnati Children's Hospital
Emory University
University of Wisconsin-Madison
University of Wisconsin-Madison
Colombia
USC / Children's Hospital Los Angeles
Children's National Hospital
Cleveland Clinic
Children's Hospital Los Angeles
Duke University
Emory University
Emory University
Emory University