Smart technology framework for medical waste optimization by integrating wireless tracking with artificial intelligence classification

In this study, we developed and validated an integrated Radio Frequency Identification-Artificial Intelligence (RFID-AI) framework to optimize medical waste management in resource-constrained healthcare settings. The system combines: (1) UHF RFID-enabled smart bins with real-time mass and environmental monitoring, (2) a fine-tuned ResNet-50 computer vision model achieving 93.1% (±2.1%) waste classification accuracy, and (3) genetic algorithm-based route optimization reducing collection distances by 18%. Implemented across four Jordanian hospitals (92-203 beds) for six months, the system demonstrated significant improvements across key metrics: Operational efficiency (30.1% reduction in collection time, P < 0.01; 81% fewer hazardous mixing incidents), staff safety (40.2% reduction in sharps injuries through AI monitoring), environmental impact (15.2% lower particulate emissions via optimized incineration scheduling), and cost-effectiveness (23.2% operational cost reduction with 14-month ROI). The framework's modular design successfully addressed institution-specific challenges, including 78.3% storage overcapacity at Princess Basma Hospital and 39.1% improper sharps disposal at Ibn Al-Nafis Hospital, while maintaining 90.3% compliance with WHO 2022 guidelines. Technical innovations included moisture-resistant RFID tags, maintaining 98.3% read accuracy in high-humidity environments and Arabic-language AI interfaces that reduced training time by 42%. These results provide empirical evidence for the viability of smart waste systems in LMICs, offering a replicable model that balances technological sophistication with practical implementation constraints. We established a new benchmark for intelligent medical waste management systems under resource limitations.