Aidena

— AI STACK RECOMMENDATION

AI Radiology Diagnostic Assistant

End-to-end stack for analyzing radiology images, generating clinical reports, and scaling from startup to production with vision models, document processing, and observability.

Stays alive for 365 days after the last visit.

Data & Analytics

AI Radiology Diagnostic Assistant

End-to-end stack for analyzing radiology images, generating clinical reports, and scaling from startup to production with vision models, document processing, and observability.

high confidence

Core Stack ℹ︎

Claude Opus 4

Primary

Best-in-class multimodal vision capabilities for analyzing complex radiology images, generating detailed clinical reports with high accuracy and nuanced medical reasoning.

$0.015/1K tokens

AWS Textract

Primary

Extracts structured data from existing radiology reports, forms, and medical documents to build training datasets and validate AI-generated reports against historical data.

$1.5/1K pages

AWS Bedrock

Primary

Managed foundation model service enabling easy model switching (Claude, Llama, Mistral) for cost optimization and A/B testing different vision models for radiology analysis.

$0.003-0.015/1K tokens

Complete the Stack ℹ︎

AgentOps

Alternative

Observability platform tracking diagnostic accuracy, report generation latency, cost per analysis, and LLM call patterns to monitor clinical quality and optimize performance.

$0/month (freemium)

DeepEval

Alternative

Automated evaluation framework for testing report quality, hallucination detection, and medical terminology accuracy before deployment to clinical workflows.

$0/month (open-source)

Baseten

Alternative

Production-grade model serving platform for deploying custom vision models and scaling inference with auto-scaling and GPU optimization for high-throughput radiology labs.

$0.10-0.50/hour (GPU)

Getting started

  1. 1Set up AWS Bedrock with Claude Opus for multimodal image analysis.
  2. 2Integrate AWS Textract to process existing radiology reports and build validation datasets.
  3. 3Create a FastAPI endpoint wrapping Bedrock calls for image upload and report generation.
  4. 4Deploy on Baseten for auto-scaling inference with GPU support.
  5. 5Instrument with AgentOps to track diagnostic accuracy, latency, and costs per analysis.
  6. 6Use DeepEval to build a CI/CD pipeline testing report quality, medical terminology, and hallucination rates.
  7. 7Implement feedback loops to fine-tune prompts based on radiologist review of generated reports.
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