The Challenge
If you ask ChatGPT about this question 1 , it provides a solid overview. Take a look.
Which can be summarized into the following theme:
Data: Large Data Space, Low Sample Size
Large Data Space Medical imaging encompasses:
- Multiple modalities: CT, MRI, ultrasound, X-ray, and specialized techniques
- Diverse manufacturers: Major vendors (GE, Siemens, Philips) plus regional systems
- Various body regions: Head, chest, abdomen, extremities
- Different pathologies: Cancer types, infections, degenerative conditions
- Patient demographics: Age, gender, and clinical history variations
- High-resolution data: CT (512×512×200 voxels), mammography (2048×1024×4 views), ultrasound (1080p×30fps×200s videos)
Low Sample Size Despite the vast data space, actual usable datasets remain limited due to:
- Privacy regulations: HIPAA and GDPR impose strict patient data protection
- Legacy infrastructure: Outdated PACS systems restrict data access
- Data silos: Institutional barriers and technical constraints limit sharing
Annotation: Sparse and Variable
Sparse Annotations Medical annotations are expensive and limited:
- Task-specific labels: No comprehensive “panoptic” annotations exist; only narrow, task-focused labels
- Notable exception: TotalSegmentator successfully aggregates multiple annotation sources 2
Variable Quality Annotation reliability faces multiple challenges:
- Bias factors
- Different levels of expertise: junior vs. senior
- Different “interpretive style”: Meticulous/Comprehensive vs. Focused/targeted
- Variance factors
- The same doctor may have different interpretations for the same image on a “moody” day 3
- For cases with ambiguity (maybe ~10%): consensus is very hard to achieve; we often settle for a few “could be” labels / “definitely not” labels
- From personal experience: overall agreement is ~80% depending on the task
- This is even with training and guidelines
- Junior staff tend to be more “instruction following” while senior staff tend to be more “self reasoning”
The Current Approaches
Today’s successful medical AI applications work by narrowing their scope rather than attempting comprehensive solutions:
Focused Implementation Strategy
- Single disease families: Lung cancer detection, not all cancers
- Specific modalities: Chest X-rays only, not mixed imaging types
- Targeted regions: Brain MRI analysis, not whole-body scans
- Population generalization: Within broad patient demographics
- Vendor compatibility: Across major equipment manufacturers
- Dataset scale: Thousands of studies (equivalent to millions of images)
This strategic limitation reduces complexity while maintaining sufficient variation for robust model training.
The Silver Lining
While the data and annotation challenges are significant, they aren’t insurmountable. The very nature of medicine and human anatomy gives us a unique advantage that traditional computer vision tasks lack: inherent structure.
Unlike the near-infinite variability of internet images, medical images are grounded in the consistent, predictable framework of human anatomy. An aorta is always next to the spine; a liver always has a similar shape and location. This anatomical consistency provides a powerful natural “prior,” or built-in knowledge, that AI can leverage.