Robust and Translational AI for Medical Imaging

The "Robust and Translational AI for Medical Imaging" Research Topic is dedicated to presentations from the 30th Conference on Medical Image Understanding and Analysis 2026 (MIUA 2026), taking place on 20–22 July 2026 in Dublin.

We aim to bring together researchers, clinicians, and industry experts to address critical challenges in medical image analysis. Topics of interest include, but are not limited to:

• Robust learning under domain shift, dataset bias, and variations in scanners, acquisition protocols, and patient populations
• Multi-centre, external, prospective, and real-world validation of medical imaging AI systems
• Uncertainty quantification, calibration, failure detection, and safe handling of out-of-distribution data
• Explainable, interpretable, and trustworthy AI for clinical imaging applications
• Fairness, subgroup performance, and mitigation of demographic and institutional bias
• Data-efficient learning, including self-supervised, weakly supervised, few-shot, and active learning methods
• Federated, distributed, and privacy-preserving learning across clinical institutions
• Foundation models, vision-language models, and multimodal learning for imaging and clinical data
• Robust image reconstruction, enhancement, registration, segmentation, classification, and artefact correction
• Generative models and synthetic data, including validation of fidelity, utility, and hallucination risk
• Human–AI collaboration, clinical workflow integration, usability, and decision-support systems
• Reproducibility, benchmarking, regulatory readiness, lifecycle monitoring, and translation into clinical practice

Further information can be found at the conference website: https://ucd.ie/medicine/miua2026/.

All manuscript types accepted by Frontiers in Medical Technology are welcomed, including Original Research, Review, Clinical Trial, Brief Research Report, Perspective, and more. Details on article types, article processing charges, and support can be found here and here.

This Research Topic welcomes submissions from all researchers working in this area, and is not limited to teams who presented papers or posters at MIUA 2026. Authors who would like their work to be considered for inclusion in this collection are invited to first submit an abstract for the Topic Editors’ review.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Medical image analysis, clinical translation, robust learning, trustworthy and explainable AI, data-efficient learning, multimodal foundation models, federated learning, privacy-preserving learning, human-AI collaboration

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.