Medical Report Analysis Pipeline
Build a HIPAA-compliant multimodal system for medical image understanding and clinical report analysis using MedSAM, RadBERT, and structured patient history
Problem Statement
We asked NEO to : Build a HIPAA-compliant system using specialized medical vision models (MedSAM for segmentation), RadBERT for report generation, and multimodal fusion of X-rays/CT scans with patient history for diagnosis assistance.
while ensuring privacy, security, and interpretability.
Task Goals:
- Assist clinicians with image-based insights (segmentation, regions of interest)
- Generate structured medical report summaries
- Fuse imaging data with patient history
- Maintain HIPAA-compliant, local-first execution
- Provide explainable, non-diagnostic assistance
Solution Overview
NEO orchestrates a multimodal medical analysis pipeline combining vision, language, and structured data:
- MedSAM for precise anatomical and pathological segmentation
- RadBERT for medical report understanding and generation
- Multimodal Fusion Layer to combine imaging features with patient history
- Clinical Output Layer for structured, explainable insights
The system is designed for decision support, not autonomous diagnosis.
Workflow / Pipeline
| Step | Description |
|---|---|
| 1. Data Ingestion | Load X-rays / CT scans and structured patient history |
| 2. Image Segmentation | MedSAM identifies organs, lesions, and regions of interest |
| 3. Feature Extraction | Extract visual embeddings from segmented regions |
| 4. Text Understanding | RadBERT processes clinical notes and historical reports |
| 5. Multimodal Fusion | Combine imaging features with patient metadata |
| 6. Report Assistance | Generate structured summaries and observations |
| 7. Compliance Controls | Local execution, audit logs, and access boundaries |
Repository & Artifacts
Generated Artifacts:
- Segmented medical images (DICOM-compatible outputs)
- Annotated regions of interest (ROIs)
- Structured clinical summaries
- Multimodal embedding representations
- Audit logs for compliance
- Reproducible inference pipelines
Technical Details
Medical Image Processing
- Model: MedSAM (medical adaptation of Segment Anything)
- Modalities: X-ray, CT
- Output: Pixel-level segmentation masks
- Benefits: Precise localization of abnormalities
Clinical Text Understanding
- Model: RadBERT
- Input: Radiology reports, patient history
- Output: Structured medical entities and summaries
- Domain Adaptation: Trained on clinical corpora
Multimodal Fusion
- Joint embedding space combining:
- Visual features from MedSAM
- Text embeddings from RadBERT
- Structured patient metadata (age, history, vitals)
- Enables contextual interpretation across modalities
Privacy & Compliance
- Local-first execution (no PHI leaves environment)
- Encrypted storage of intermediate artifacts
- Access-controlled pipelines
- Full auditability of inference steps
Results
- Segmentation Accuracy: High Dice scores across organs and lesions
- Report Quality: Clinically coherent summaries aligned with radiology standards
- Interpretability: Clear mapping between image regions and textual insights
- Compliance: Fully HIPAA-aligned architecture
The system provides assistive intelligence without replacing clinician judgment.
Best Practices & Lessons Learned
- Always separate diagnosis assistance from decision-making
- Prefer domain-specific models over general-purpose LLMs
- Maintain strict data locality for PHI
- Log every inference step for auditability
- Design outputs for clinician readability, not raw predictions
Next Steps
- Add longitudinal patient history tracking
- Support additional modalities (MRI, ultrasound)
- Integrate clinical guideline references
- Enable interactive clinician feedback loops
- Add uncertainty estimation for model outputs
References
- GitHub Repository
- MedSAM: https://github.com/bowang-lab/MedSAM
- RadBERT: https://github.com/rajpurkar/radbert
- HIPAA Guidelines: https://www.hhs.gov/hipaa/index.html