An all-in-one foundation model for multimodal medical image restoration and enhancement

Abstract

Medical image quality is essential for reliable clinical diagnosis and downstream artificial intelligence analysis, yet routine images are often degraded by noise, motion artefacts, low contrast, low-dose acquisition or modality-specific artefacts. Here we present AMIFound, an all-in-one foundation model for medical image enhancement and restoration across computed tomography, magnetic resonance imaging, positron emission tomography, endoscopy, ultrasound, fundus imaging and X-ray. AMIFound is trained and evaluated on 3.76 million paired high-quality and degraded images spanning 7 representative degradation patterns. A shared encoder captures modality and degradation cues, while a dynamically routed expert restoration pool adapts the enhancement pathway for each input. Across 7 modalities, AMIFound outperforms 38 task-specific and generalist baselines on full-reference image quality metrics, clinician assessment and robustness tests under combined degradations as well as on naturally degraded clinical images. Enhanced images further improve 44 disease detection tasks, tumour stage prediction and survival risk stratification in non-small-cell lung cancer, including improved individual-level prognostic ranking. These results support AMIFound as a domain-specific foundation model for improving low-quality clinical images and strengthening downstream medical artificial intelligence workflows while preserving clinically relevant information. Cao et al. present AMIFound, a foundation model that unifies medical image restoration across seven imaging modalities, delivering robust enhancement across diverse degradations and preserving clinically relevant information for diagnosis.