The Challenge
If you ask ChatGPT about this question 1 , it would give a pretty good answer, take a look.
Which can be summarized into the following theme:
- huge data space
- different modalities: CT, MRI, US, X-ray, etc.
- different manufacturers: Big 3 (GE, Siemens, Philips), local vendors, etc.
- different body regions: head, chest, abdomen, etc.
- different diseases: different family of cancer, infection, etc.
- different patient population: age, gender, etc.
- very high resolution: CT scans (512×512×200), mammography (2048×1024×4), ultrasound video (1080p×30fps×200s)
- data is scarce
- Privacy regulations: strict laws require patient data protection
- Poor infrastructure: legacy systems at most medical sites limit data access
- Limited data sharing: regulatory and technical barriers make data expensive to collect (though this is gradually improving)
- annotations are expensive and sparse
- No comprehensive “panoptic” annotations exist, only task-specific labels
- Notable exception: TotalSegmentator successfully combines multiple annotation sources 2
The Current Approaches
Current successful applications focus narrowly:
- single disease family
- single imaging modality
- single body region
- generalize across patient populations
- generalize to major equipment vendors
- using thousands of studies .
This focused approach works because it reduces the complexity of the data space while still providing sufficient variation for robust model training.
The silver lining
Most healthy people share similar anatomical structures, creating intrinsic patterns in medical images. We can leverage this consistency through:
- Anatomical structure: Human anatomy provides a reliable spatial framework
- Pattern recognition: Abnormalities typically begin with characteristic “blob-like” appearances
- Pathological reasoning: Disease progression follows predictable biological processes
Looking Forward
Despite current limitations, the medical imaging field is uniquely positioned to benefit from AI advances. The combination of anatomical priors, improving data sharing, and sophisticated AI models promises significant breakthroughs in diagnostic accuracy and accessibility.
The key is balancing the power of large-scale learning with the structured knowledge that medical experts have developed over centuries of practice.