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  HealthLens

HealthLens - Making Medical Imaging Accessible

Inspiration

Healthcare can be overwhelming, especially when patients receive complex medical scan results filled with technical jargon they can't understand and radiologists have to face unfamiliar scan results. We were inspired by the gap between advanced AI diagnostic capabilities and patient comprehension alongwith the global shortage of capable radiologists. Too often, patients leave medical appointments confused about their X-rays, CT scans, or MRIs, leading to anxiety and poor health decisions. Likewise, quite a few times, it so happens that the specialist is not entirely aware as to what the scans portray and requires peer consultation. We envisioned a world where cutting-edge medical AI could bridge this communication gap, empowering patients with clear, understandable explanations of their medical imaging results and seamlessly integrating the existing medical information out there in the form of quick and easy access for the radiologists.

What it does

HealthLens transforms complex medical scan analysis into patient-friendly explanations that anyone can understand. The platform analyzes medical images (X-rays, CT scans, MRIs) using advanced computer vision models, detects potential medical findings, and then generates comprehensive, easy-to-read reports. Unlike traditional radiology reports filled with medical terminology, HealthLens provides explanations in plain English, complete with context about what findings mean, potential next steps, and reassuring guidance for patients to discuss with their healthcare providers.

How we built it

We built HealthLens using a modern tech stack centered around FastAPI for the backend API. The core architecture integrates multiple AI components:

• Computer Vision: MONAI for 3D medical imaging, TorchXRayVision for 2D X-ray analysis, and YOLO models for general detection
• AI Consultation: Perplexity's Sonar API for research-backed medical insights and patient explanations
• Backend: FastAPI with Python for robust API endpoints and real-time processing
• File Processing: Support for DICOM, NIfTI, PNG, and JPG medical image formats
• Documentation: Comprehensive patient explanation guides and user-friendly analysis scripts

The system processes medical scans through multiple AI models, combines the technical findings, and uses advanced language models to generate patient-appropriate explanations while maintaining medical accuracy.

Challenges we ran into

The biggest challenge was balancing medical accuracy with patient accessibility. Medical AI models output highly technical results that needed to be translated without losing critical information or creating false reassurance. We also faced integration complexities when combining multiple AI frameworks (MONAI, TorchXRayVision, Perplexity) into a cohesive system. Managing large medical AI model downloads and ensuring HIPAA-compliant data handling added additional complexity. Finally, creating explanations that were informative yet appropriately cautious—encouraging patients to consult healthcare providers rather than self-diagnose—required careful prompt engineering and extensive testing.

Accomplishments that we're proud of

We successfully created a working medical AI platform that democratizes access to medical imaging interpretation. Our system can analyze real medical scans and generate genuinely helpful patient explanations. We're particularly proud of:

• Multi-modal support: Successfully integrating 2D and 3D medical imaging analysis
• Patient-centered design: Creating explanations that reduce anxiety while providing valuable insights
• Technical integration: Seamlessly combining multiple AI frameworks into a single, user-friendly API
• Real-world testing: Successfully analyzing actual medical scans with detailed, comprehensible results
• Comprehensive documentation: Building user guides that make the technology accessible to non-technical users

What we learned

This project taught us the immense complexity of medical AI and the critical importance of responsible AI deployment in healthcare. We learned that technical accuracy alone isn't sufficient—the presentation and communication of AI findings is equally crucial. We gained deep insights into medical imaging formats, the nuances of different AI model architectures, and the ethical considerations of patient-facing medical technology. Most importantly, we learned that the most sophisticated AI is only valuable if it can be understood and acted upon by the people it's meant to help.

What's next for HealthLens

Our roadmap includes expanding to more medical imaging types (ultrasounds, mammograms, pathology slides), implementing real-time collaborative features for healthcare providers, and developing mobile applications for easier patient access. We plan to integrate with electronic health record systems and add multi-language support to serve diverse populations. Long-term, we envision HealthLens becoming a comprehensive medical AI platform that not only explains results but also tracks patient progress over time, provides personalized health insights, and facilitates better doctor-patient communication through AI-enhanced medical consultations.

Built With

• css
• fastapi
• html
• javascript
• monoai
• numpy
• python
• pytorch
• react
• typescript