MedDiagnose AI - Radiology Image Analysis Assistant

The Challenge

Hospital radiologists faced 3-week backlogs for routine scans, leading to delayed diagnoses and frustrated patients. The shortage of qualified radiologists was creating a healthcare bottleneck that affected patient outcomes.

The Solution

Created a computer vision system using convolutional neural networks to assist in X-ray and CT scan analysis. Trained on 100K+ anonymized medical images, implemented DICOM file processing, and built validation workflows for radiologist review and approval.

Technologies Used

• Python
• TensorFlow
• OpenCV
• DICOM
• FastAPI
• PostgreSQL

The Results

Reduced initial scan analysis time by 78%, identified 96% of critical findings requiring immediate attention, and helped clear the backlog from 3 weeks to 4 days. Now assists with 500+ scans weekly across 3 hospitals.

Key Metrics

• Time Reduction: 78%
• Critical Detection: 96%
• Scans Weekly: 500+

Lessons Learned

This project reinforced the importance of user-centered design, iterative development, and measuring impact through real metrics rather than vanity numbers. The biggest challenge wasn't technical—it was ensuring the solution actually solved the human problem we set out to address.

Want to discuss this project or collaborate on something similar? Feel free to reach out!