Full Papers
Original research contributions with substantial technical depth and experimental validation.
Machine Learning/CORE Unranked
ML-CDS2026 2026
ML-CDS 2026: Multimodal Learning and Fusion Across Scales for Clinical Decision Support
ML-CDS @ MICCAI 2026 is a workshop focused on machine learning for clinical decision support, held in conjunction with the MICCAI conference. It aims to bring together researchers and practitioners to present and discuss innovative machine learning methods applied to clinical decision-making, with an emphasis on real-world healthcare applications and challenges.
Key deadlines
Verified September 9, 2026Abstract registration
June 24, 2026
AoE
Full paper
June 29, 2026
AoE
Conference timeline
Submission and decisions
Abstract registrationKey deadline
June 24, 2026 · AoE
Full paperKey deadline
June 29, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Short Papers
Preliminary results, novel ideas, or work in progress with clear potential for discussion and feedback.
Case Studies
Real-world applications of machine learning in clinical decision support, highlighting challenges and lessons learned.
System Demonstrations
Working prototypes or tools for clinical decision support, with emphasis on usability and clinical integration.
Research areas in scope
01
Topics of Interest
Machine learning for clinical decision supportExplainable AI in clinical settingsFederated learning for healthcareTemporal modeling and time-series analysis in clinical dataMultimodal data integration (e.g., imaging, EHR, genomics)Clinical risk prediction and prognosisReal-time decision support systemsBias, fairness, and ethical considerations in clinical MLValidation and reproducibility of clinical ML modelsTransfer learning and domain adaptation in healthcareHuman-in-the-loop and clinician-AI collaborationML for personalized medicine and precision healthML for clinical workflow optimizationData scarcity and small-data learning in clinical contextsML for rare diseases and underrepresented populations
Policies worth checking twice
- Submissions must be anonymized for double-blind review.
- Papers must not exceed 10 pages for full papers and 5 pages for short papers, including references and appendices.
- Dual submission to other conferences or journals is not allowed during the review period.
- All submissions must include a statement on the use of AI tools in the writing process.
- Author lists and affiliations cannot be changed after the submission deadline.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026