AI Agents for Biomedical Imaging and Multimodal Clinical Data

Biomedical imaging AI has produced strong methods for segmentation, registration, reconstruction, detection, and report generation, yet most systems remain organized around fixed inputs and outputs (a structure that fails to capture the complexity of real clinical workflows, where images must be integrated with reports, laboratory values, EHR data, waveforms, and longitudinal patient context). This workshop focuses on AI agents for biomedical imaging and multimodal clinical data, bringing together researchers in machine learning, computer vision, biomedical imaging, and clinical AI to define shared task definitions, evaluation protocols, and benchmarks for agentic image analysis systems. Topics include tool-using agents for image measurement and annotation, multimodal grounding across imaging and clinical data, human-in-the-loop workflows, and reproducible evaluation frameworks. The workshop is paired with a special issue of the Machine Learning for Biomedical Imaging (MELBA) journal, in which authors of accepted workshop papers are invited to submit extended manuscript versions for independent peer review, with an archival pathway coordinated by organizers who hold editorial roles at MELBA.