An AI agent that reads and explains medical images
A multimodal medical-imaging diagnosis agent that analyzes X-ray, MRI, CT and ultrasound images, produces structured, standards-aligned reports, and translates findings into language patients can understand.
The problem
Radiology faces diagnostic bottlenecks, uneven access to specialist expertise, and a communication gap: patients rarely understand their own results. Rapid, consistent initial assessment could ease all three — but only if it is reliable and well-communicated.
Approach
The agent provides an end-to-end workflow: advanced multimodal analysis of medical images, structured reporting that follows medical standards, and a plain-language explanation layer for patients — with validation and error handling throughout.
- Multimodal image analysis with Google's latest Gemini Flash model,
- Support for X-ray, MRI, CT and ultrasound
- Structured, standards-aligned diagnostic reports
- Patient-friendly translation of complex findings
Architecture
A Streamlit application orchestrates image intake, model analysis and report generation, with Google's latest Gemini Flash model providing the vision-language reasoning and robust validation guarding the outputs.
- Streamlit application layer
- Google Gemini Flash vision-language model
- Validation and error-handling safeguards
Deployment
The agent is deployed as an interactive application on AWS, available as a live demo that showcases the full intake-to-report flow.
Business value
It demonstrates how multimodal generative AI can support faster initial assessment, extend scarce radiology expertise, and close the patient-communication gap — an assistive tool designed to augment clinicians, not replace them.
Lessons learned
In clinical contexts, communication and validation matter as much as raw model accuracy.
Structured reporting turns a model's output into something a professional can actually act on.
Multimodal models make it feasible to cover several imaging modalities within one coherent workflow.