Inventory
Hospital stock analyzed for sustainable, affordable swaps.
AI/ML2025
EcoMed AI
Intelligent AI system built for Philly {Codefest} '25 for greener healthcare. Analyzes hospital inventories to recommend affordable, sustainable supplies via a BOM Processing System, and uses image classification with ResNet50 to sort waste and avoid costly hazardous-waste disposal.
Gallery
Waste vision
ResNet50 classifies waste to reduce hazardous mis-sorts.
Codefest ’25
Shipped for Philly {Codefest} with a greener-care brief.
Product
EcoMed AI helps hospitals cut cost and environmental waste. It recommends sustainable supplies from inventory data and classifies waste with computer vision so hazardous streams are handled correctly—and expensive mistakes are avoided.
Problem
Healthcare supply chains overspend on non-sustainable products, and mis-sorted waste drives hazardous-disposal costs.
Solution
A BOM Processing System for greener purchasing plus ResNet50 vision for waste sorting—built at Philly {Codefest} ’25.
Highlights
- BOM Processing System for sustainable supply recommendations
- ResNet50 image classification for waste sorting
- Built at Philly {Codefest} '25
Specs
- Domain
- Healthcare / sustainability
- Event
- Philly {Codefest} ’25
- Vision model
- ResNet50
- Supply engine
- BOM Processing System
- Language
- Python
- Status
- Shipped
How it works
- 1Load hospital inventory into the BOM Processing System.
- 2Review sustainable supply recommendations and cost tradeoffs.
- 3Capture or upload waste images for classification.
- 4Route items using ResNet50 labels to avoid hazardous-waste mischarges.
Achievements
- Built and demoed at Philly {Codefest} ’25
- Dual pipeline: supply intelligence + waste vision
- Targets both cost savings and greener hospital operations
Stack
PythonResNet50Image Classification
Core StackBackend & DatabasesData & Machine Learning