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

Medical diagnosis support systems must operate under strict regulatory constraints, handle complex multimodal data, and provide transparent, assistive outputs rather than autonomous decisions.

The challenge is to combine:

High-resolution medical imaging (X-rays, CT scans)
Unstructured clinical text and patient history
Domain-specific medical reasoning

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

GitHub Repository:
Medical Report Analysis Pipeline by NEO

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