Project Info
Inspiration
Hack the world a better place! - This motto drives our vision to make medical imaging more accessible, trustworthy, and reassuring for everyone, regardless of their background. Around the world, many patients - especially in less wealthy areas - face long waits for expert analysis, leading to anxiety and uncertainty. By using multiple AI models, we provide an instant, diverse opinions, increasing trust and transparency in AI-assisted healthcare. We aim to support the UN goals for health (Goal 3) and equality (Goal 10) by ensuring that people from all demographics, have better access to medical insights. We aim to empower patients with clarity, multiple perspectives, and a greater sense of safety in their healthcare journey.
What it does
XrayVision is a web app that empowers patients by providing instant diagnostic insights from multiple AI models. By leveraging an ecosystem of diverse AI models, we ensure greater trust, reduced uncertainty, and more reliable results compared to a single-model approach. Our platform not only detects diseases and pathologies but also offers AI-driven explanations, generates comprehensive reports, and provides personalized recommendations for next steps. It’s designed to be a supportive tool for gaining feedback or a second opinion.
How we built it
Our team developed XrayVision by combining cutting-edge AI technologies with robust software engineering principles to create a seamless and reliable platform. The front-end and back-end are built in Python using Streamlit, ensuring a user-friendly and efficient interface. We integrated multiple state-of-the-art foundation models (FM), trained on billions of text and image pairs—including models from Mistral-AI and Perplexity—to enhance diagnostic accuracy and trust. To power our AI models, we leverage the NVIDIA GeForce RTX 3090 (24GB), enabling high-performance inference for real-time disease detection and explanation.
Challenges we ran into
Balancing high performance and real-time processing capabilities without compromising user experience was complex. We are new to front-end development, so creating a front-end which shows all features in a nice way took more time than expected. Combining models that have different environment requirements
Accomplishments we're proud of
our teamwork and especially the result of our project. the development of the first patient-focused app that integrates an ecosystem of multiple foundation models to detect diseases from chest x-rays, but even more important help a patient to understand complicated reports and diagnosis We could test our platform on a friend's x-ray and IT WORKED -> disease detection and localization :) Contributing to global health with cutting edge technologies as our platform is easy accessible on any device.
What we learned
Teamwork makes the dream work! <3 Push & Pull even more often if you work at the same time on the same project.
What's next
While XrayVision is currently focusing on analysis of chest x-rays, the next step will go one step further from 2D to 3D, we want to include CT and MRI disease analysis.
XRayVision
Hack the World a Better Place!
🌲 TREEHACKS - 2025
XRayVision is a cutting-edge web app designed to empower patients by providing instant diagnostic insights from multiple AI models. By harnessing an ecosystem of diverse AI-powered analyses, we enhance trust, reduce uncertainty, and deliver more reliable results than any single-model approach. Our platform not only detects diseases and pathologies but also provides AI-driven explanations, generates comprehensive reports, and offers personalized recommendations for next steps. Whether seeking a second opinion or validating AI-driven diagnostics, XRayVision is here to support you.
AI Models in Use
- Mistral-AI (Mistral-7B-Instruct-v0.3) → Radiology report explanation
- Perplexity → Personalized recommendations based on detected diseases
- TorchXRayVision, CheXagent, MedImageInsight → Disease prediction
- CheXagent → Disease localization & radiology report generation
Our Motivation
Hack the world a better place! This is the driving force behind our mission to make medical imaging more accessible, trustworthy, and reassuring for everyone, regardless of their background. Across the globe, many patients—especially in underprivileged regions—face long waits for expert analysis, leading to stress and uncertainty. By leveraging multiple AI models, we provide instant and diverse medical insights, fostering trust and transparency in AI-assisted healthcare.
We proudly align with the UN Sustainable Development Goals, particularly Goal 3 (Good Health & Well-Being) and Goal 10 (Reduced Inequalities), ensuring that people from all demographics gain better access to critical medical insights.
XRayVision empowers patients with clarity, multiple perspectives, and a greater sense of security in their healthcare journey.
Analysis
View
Metric
- 30
- 21
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- Hugging FaceIn code
- PythonIn code
- PyTorchIn code
- StreamlitIn code
- TensorFlowIn code
- Mistral AIClaimed
5 of 6 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
271 KB
Source files
53
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
loufay/XrayVision
56 files · 752 KB · @ 86ec799
Structure
Application logic
17 files · 30%Domain rules, services and shared utilities.
Data & schema
34 files · 61%Schema definitions, migrations and data access.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- Python99%
- Markdown1%
- YAML0%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
requirements.txt
pypi · 206- absl-py
- aiohappyeyeballs
- aiohttp
- aiosignal
- altair
- asttokens
- astunparse
- async-timeout
- attrs
- backcall
- beautifulsoup4
- bleach
- blinker
- cachetools
- certifi
- charset-normalizer
- click
- cloudpickle
- +188 more
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
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