AI-Powered Diagnostic Assistance Platform

AIHealthcareSecurity

15%

Accuracy Increase

60%

Faster Reporting

100%

HIPAA Compliant

The Challenge

Pain Points

• Legacy bottlenecks
• Security risks
• Scalability issues

Context

A network of diagnostic centers wanted to leverage AI to assist radiologists in analyzing X-rays and MRI scans. Data privacy was the paramount concern.

The Problem

Radiologists were overwhelmed by volume, leading to burnout and delayed reports. Generic AI models could not be used due to patient data privacy regulations (HIPAA/GDPR).

The Solution

We built a private, air-gapped RAG (Retrieval-Augmented Generation) pipeline. The system anonymizes data before processing and uses a fine-tuned open-source model hosted within the client's VPC.

Technology Stack

PyTorchLlama-3LangChainMilvusDocker

Implementation Process

1
Data anonymization protocol development.
2
Fine-tuning of Llama-3 model on medical datasets.
3
Development of RAG pipeline for medical literature.
4
Integration with existing PACS workflow.
5
User acceptance testing with senior radiologists.

“It acts as a second pair of eyes that never gets tired. The efficiency gains have been transformative for our patient care.”

— Medical Director, Diagnostic Network

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