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RxLens: Multi-agent LLM-powered scan and order for pharmacy

By Akshay Jagatap, Srujana Merugu, Prakash Mandayam Comar
2025
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Automated construction of shopping cart from medical prescriptions is a vital prerequisite for scaling up online pharmaceutical services in emerging markets due to the high prevalence of paper prescriptions that are challenging for customers to interpret. We present RxLens, a multi-step end-end Large Language Model (LLM)-based deployed solution for automated pharmacy cart construction comprising multiple steps: redaction of Personal Identifiable Information (PII), Optical Character Recognition (OCR), medication extraction, matching against the catalog, and bounding box detection for lineage. Our multi-step design leverages the synergy between retrieval and LLM-based generation to mitigate the vocabulary gaps in LLMs and fuzzy matching errors during retrieval. Empirical evaluation demonstrates that RxLens can yield up to 19% - 40% and 11% - 26% increase in Recall@3 relative to SOTA methods such as Medical Comprehend and vanilla retrieval augmentation of LLMs on handwritten and printed prescriptions respectively. We also explore LLM-based auto-evaluation as an alternative to costly manual annotations and observe a 76% - 100% match relative to human judgements on various tasks.
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