@inproceedings{jagatap-etal-2025-rxlens,
title = "{R}x{L}ens: Multi-Agent {LLM}-powered Scan and Order for Pharmacy",
author = "Jagatap, Akshay and
Merugu, Srujana and
Comar, Prakash Mandayam",
editor = "Chen, Weizhu and
Yang, Yi and
Kachuee, Mohammad and
Fu, Xue-Yong",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-industry.63/",
doi = "10.18653/v1/2025.naacl-industry.63",
pages = "822--832",
ISBN = "979-8-89176-194-0",
abstract = "Automated construction of shopping cart frommedical prescriptions is a vital prerequisite forscaling up online pharmaceutical servicesin emerging markets due to the high prevalence of paper prescriptionsthat are challenging for customers to interpret.We present RxLens, a multi-step end-end Large Language Model (LLM)-based deployed solutionfor automated pharmacy cart construction comprisingmultiple 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 generationto 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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<abstract>Automated construction of shopping cart frommedical prescriptions is a vital prerequisite forscaling up online pharmaceutical servicesin emerging markets due to the high prevalence of paper prescriptionsthat are challenging for customers to interpret.We present RxLens, a multi-step end-end Large Language Model (LLM)-based deployed solutionfor automated pharmacy cart construction comprisingmultiple 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 generationto 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.</abstract>
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%0 Conference Proceedings
%T RxLens: Multi-Agent LLM-powered Scan and Order for Pharmacy
%A Jagatap, Akshay
%A Merugu, Srujana
%A Comar, Prakash Mandayam
%Y Chen, Weizhu
%Y Yang, Yi
%Y Kachuee, Mohammad
%Y Fu, Xue-Yong
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-194-0
%F jagatap-etal-2025-rxlens
%X Automated construction of shopping cart frommedical prescriptions is a vital prerequisite forscaling up online pharmaceutical servicesin emerging markets due to the high prevalence of paper prescriptionsthat are challenging for customers to interpret.We present RxLens, a multi-step end-end Large Language Model (LLM)-based deployed solutionfor automated pharmacy cart construction comprisingmultiple 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 generationto 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.
%R 10.18653/v1/2025.naacl-industry.63
%U https://aclanthology.org/2025.naacl-industry.63/
%U https://doi.org/10.18653/v1/2025.naacl-industry.63
%P 822-832
Markdown (Informal)
[RxLens: Multi-Agent LLM-powered Scan and Order for Pharmacy](https://aclanthology.org/2025.naacl-industry.63/) (Jagatap et al., NAACL 2025)
ACL
- Akshay Jagatap, Srujana Merugu, and Prakash Mandayam Comar. 2025. RxLens: Multi-Agent LLM-powered Scan and Order for Pharmacy. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track), pages 822–832, Albuquerque, New Mexico. Association for Computational Linguistics.