RadReport AI: Chest X-ray Reports

From chest X-rays to clinical reports — how multimodal AI is reshaping diagnostic workflows.

Year

2025

Team

Rakesh Nagaragatta Jayanna, Pavankumar Umesh Managoli

Tech-Stack

ViT, GPT-2, Hugging Face AutoTokenizer, PyTorch

Location

Germany

Published on: 6. Juni 2025

At iiterate Technologies, we build intelligent tools that solve real-world challenges — from document workflows to diagnostics. One of our recent experimental projects, RadReport AI, explores how multimodal AI can assist radiologists in generating consistent, accurate reports from chest X-rays.

This prototype system combines computer vision and natural language processing to generate clinical summaries from medical images — in seconds.

Why We Built This

Radiologists spend significant time interpreting chest X-rays and manually writing structured reports. While accuracy is critical, the process is repetitive and time-consuming. Our goal with RadReport AI was to explore how AI can help pre-fill initial summaries, allowing radiologists to review, revise, and finalize — rather than start from scratch.

It’s not about replacing expertise. It’s about amplifying it.

How It Works

RadReport AI is built using a multimodal pipeline that links visual understanding with text generation. The model was trained on the Indiana University Chest X-ray dataset, which includes over 7,000 labeled images and reports.

Core tech components:

Vision Encoder: ViT (vit-base-patch16-224)
Language Decoder: GPT-2
Tokenizer: Hugging Face AutoTokenizer
Training Framework: PyTorch + Transformers
UI Deployment: Streamlit, hosted on Hugging Face Spaces

Once a chest X-ray is uploaded, the model detects relevant patterns in the image and translates them into clinical findings using a GPT-based decoder.

What It Can Do

Here’s what RadReport AI currently supports:

Detect and interpret chest X-ray features
Generate concise "Impression" summaries
Validate outputs using BLEU, ROUGE, and METEOR
Run inference via a live, interactive demo
Support explainable outputs using attention-based methods (upcoming)

Evaluation Scores:

BLEU: 0.51
ROUGE-1: 0.55
METEOR: 0.53

These results reflect a strong overlap with expert-generated reports.

Real-World Potential

RadReport AI is a proof of concept, but its use cases are tangible:

Radiology Clinics: Quick impressions that radiologists can edit and approve
Medical Training: Helps students map visual data to clinical terms
Healthcare AI Research: Testbed for multimodal diagnostic pipelines

It’s publicly hosted, requires no installation, and is open for testing and feedback.

What’s Next

We’re currently working on:

Multi-section reports (e.g., Findings + Impressions)
Attention visualizations for more transparency
Ontology integration (e.g., RadGraph alignment)
Expansion into multilingual reporting

If your clinic, research lab, or university is exploring AI-supported radiology, we’d be happy to collaborate.

Final Thoughts

RadReport AI is a small but meaningful step in exploring how vision-language models can support medical professionals. At iiterate, we see AI as a tool to amplify human intelligence, not replace it — and we build with that mindset.

Stay tuned as we continue to push the boundaries of applied AI.