Radi.ai

A platform designed to empower radiologists with AI-driven insight to transform the speed and accuracy of medical imaging.

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AI-Assisted Radiology Workflow System

About the Project

Inspiration

This project was inspired by the growing need to improve efficiency and accuracy in healthcare, particularly in radiology. Medical imaging is one of the most data-intensive and time-sensitive areas in medicine, where delays or missed diagnoses can have serious consequences.

We were especially motivated by:

  • The increasing workload on radiologists
  • The potential of AI to assist (not replace) clinicians
  • The lack of seamless integration between AI tools and existing hospital workflows

Rather than building just another diagnostic model, we wanted to design a complete system that fits naturally into how hospitals already operate.

What We Learned

Through this project, we gained a deeper understanding of both the technical and business sides of healthcare AI.

Technical:

  • How Convolutional Neural Networks (CNNs) can be applied to medical imaging
  • The importance of explainability
  • Handling medical imaging formats (DICOM) and integrating with systems like PACS
  • Designing scalable pipelines for real-time inference using queues and APIs

Business & Product:

  • Why healthcare products must prioritize workflow integration over raw accuracy
  • The complexity of selling to hospitals (long sales cycles, compliance, ROI justification)
  • The importance of realistic market sizing

How We Built the System (Conceptually)

We designed an end-to-end AI-assisted radiology workflow system focused on lung X-ray analysis.

Workflow Overview:

  1. Patient undergoes a chest X-ray
  2. Doctor inputs relevant medical history
  3. AI model analyzes the scan for abnormalities
  4. System outputs:
    • Severity score
    • Probable diagnosis
    • Visual explanation (heatmap)
  5. Doctor reviews and approves results
  6. Final report is automatically added to the patient’s chart

AI Model

We proposed using a Convolutional Neural Network (CNN) such as ResNet or DenseNet for image analysis.

The model performs multi-label classification:

$$ P(y_i \mid X) = \sigma(f_\theta(X)) $$

System Architecture

We designed a modern, scalable tech stack:

  • Frontend: React + TypeScript
  • Backend: FastAPI (Python)
  • AI/ML: PyTorch + MONAI
  • Storage: AWS S3 / Azure Blob
  • Database: PostgreSQL
  • Workflow Queue: RabbitMQ
  • Integration: HL7/FHIR + DICOM (PACS systems)
  • Deployment: Docker + Cloud (Azure/AWS)

The system is built around a pipeline architecture:

  • Ingestion → Processing → Inference → Reporting → Integration

Challenges We Faced

1. Scope Creep

Initially, our idea included:

  • Diagnosis
  • Treatment planning
  • Scheduling optimization
  • 3D visualization

We realized this was too broad and refined the system to focus on:

  • AI-assisted detection + workflow integration

2. Explainability & Trust

In healthcare, accuracy alone is not enough.

We had to incorporate:

  • Model transparency
  • Visual explanations
  • Human-in-the-loop approval

3. Business Viability

Designing the system required understanding:

  • Cost structure (high fixed, low marginal)
  • Pricing strategy (subscription + per scan)
  • Market entry challenges (slow adoption, regulation)

Final Thoughts

This project evolved from just “AI for X-rays” into a full healthcare system design.

The biggest takeaway was:

The value of AI in healthcare is not just in prediction, but in how well it integrates into real-world workflows.

By focusing on efficiency, explainability, and system design, we created a concept that is both technically sound and practically viable.

Built With

  • figma
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