Foundation Models for Medical Image Segmentation: Technical Progress, Clinical Translation, and Governance Challenges

Abstract

Medical image segmentation is moving from task-specific convolutional models toward foundation models that can be adapted across organs, modalities, and clinical tasks with fewer manual labels. This transition has been accelerated by self-supervised pretraining, vision-language learning, and promptable segmentation frameworks such as the Segment Anything Model and its medical derivatives. However, the clinical value of these systems cannot be inferred from technical novelty alone. Medical images differ from natural images in dimensionality, intensity statistics, acquisition protocols, disease prevalence, and safety requirements, and recent evaluations show that naive zero-shot transfer remains inconsistent across modalities and lesion types. This narrative review synthesizes literature published up to May 22, 2026, on foundation models for medical image segmentation, with emphasis on technical evolution, application scenarios, validation strategies, and governance needs. Current evidence suggests that foundation models are most promising when they are deployed as interactive, auditable components within human-in-the-loop workflows, where they can reduce annotation burden, support rapid draft segmentation, and improve consistency across large imaging studies. Their translation into routine practice requires external validation, uncertainty-aware quality control, prospective workflow evaluation, bias assessment, and transparent reporting under medical AI guidelines. Future work should prioritize patient-level multimodal modeling, 3D and longitudinal segmentation, federated evaluation, and clinically meaningful endpoints rather than isolated benchmark gains.