MediCycle AI: A Smart Framework for Safe and Sustainable Redistribution of Surplus Medicines

Abstract: Unused and surplus medicines accumulate across households, hospitals, and non-governmental organizations, creating environmental and public-health burdens while access to essential medicines remains uneven. Take-back and reverse-logistics programmes concentrate on collection and disposal governance, demand forecasting targets procurement, and verification, expiry recognition, and AI decision support are demonstrated in isolation. This paper proposes MediCycle AI, an integrated, auditable framework for the safe, expiry-aware, demand-driven redistribution of eligible surplus medicines.It combines image-based verification with OCR for label and expiry extraction; demand forecasting using random forest and XGBoost, with LSTM only where the dataset justifies it; inventory management with near-expiry detection; a transparent multi-factor recipient-matching and priority-scoring model; and controlled generative and agentic AI layers under retrieval grounding and mandatory human approval. Hard safety constraints— expired or unverifiable medicines are never redistributed—are separated explicitly from soft prioritization factors such as demand, urgency, shelf life, quantity, distance, and recipient priority. The paper presents the architecture, database design, protocol, and evaluation metrics. As of submission, implementation and quantitative results are pending; the manuscript reports an evaluation plan and expected outcomes rather than empirical results, and positions the framework strictly as decision support requiring professional and regulatory validation.

Keywords: Surplus Medicine Redistribution, Pharmaceutical Reverse Logistics, Demand Forecasting, XGBoost, OCR, Computer Vision, Medicine Verification, Recipient Matching, Generative AI, Agentic AI, Healthcare Supply Chain, SDG.