5.5 New Paradigms: SAM & Generative AI
In recent years, medical imaging AI has been shifting from “train one model per task” toward foundation models and generative priors.
SAM in medical imaging
Common uses:
- interactive annotation acceleration (point/box prompts)
- semi-automatic labeling pipelines (model suggestion + human correction)
- bootstrapping downstream specialist models (e.g., nnU-Net) with cheaper labels
⚠️ Domain gap matters
SAM is largely trained on natural images. For medical images, low-contrast boundaries, tiny lesions, and 3D volumes often require adaptation and careful validation.
Generative AI: denoising, reconstruction, and synthesis
Typical applications:
- low-dose CT denoising / artifact reduction
- accelerated MRI reconstruction
- data augmentation / long-tail synthesis
⚠️ Hallucination risk
In medical imaging, “looks plausible” is not enough. Generative models must be evaluated for false positives/negatives and integrated with QA and uncertainty-aware checks.