DRISHTI is a novel RFID-vision integrated assistive medication-verification system that combines RFID contactless scanning, quantized AI-based vision processing, and adaptive audio feedback to provide comprehensive medication-safety assurance. The architecture integrates an MFRC522 RFID reader for rapid drug-container identification, a Raspberry Pi–mounted camera running a quantized Gemma3-4B vision model for prescription-document analysis, and a hierarchical validation engine employing confidence-weighted scoring across five critical safety dimensions. Operating entirely offline, the system processes compressed medication data through multi-criteria classification while preserving user privacy and eliminating cloud dependencies. In evaluations across 149 test scenarios, DRISHTI achieved 86.57% overall accuracy and 100% detection of safety-critical cases, including expired medications, dosage mismatches, and drug interactions. The system delivers sub-millisecond response times with real-time, urgency-differentiated audio feedback, offering a practical solution for enhancing independence and reducing healthcare risks for visually impaired individuals.

Clinical federated learning faces critical challenges from statistical heterogeneity across healthcare institutions and privacy requirements for sensitive medical data. This work implements the foundational components of FedCliMask and proposes a comprehensive framework for privacy-preserving federated learning in clinical settings that combines ontology-guided semantic masking with context-aware federated aggregation. Our framework addresses the dual challenges of privacy preservation and statistical heterogeneity through two key innovations: (1) ontology-guided semantic masking using UMLS hierarchies to provide graduated privacy protection while preserving clinical semantics, and (2) context-aware federated aggregation that considers hospital-specific features including medical specialties, data complexity, privacy levels, and data volume. The semantic masking component is implemented and evaluated on synthetic clinical data, demonstrating effective privacy-utility tradeoffs across four masking levels. The context-aware analysis component is also implemented successfully profiling 12,996 synthetic clinical notes across 6 diverse hospitals to demonstrate meaningful hospital differentiation. The complete framework is designed to enable privacy-preserving clinical trial recruitment through federated learning while adapting to institutional heterogeneity.