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arXiv · 2505.17059

Medalyze: Lightweight Medical Report Summarization Application Using FLAN-T5-Large

Van-Tinh Nguyen·Hoang-Duong Pham·Thanh-Hai To·Cong-Tuan Hung Do·Thi-Thu-Trang Dong·Vu-Trung Duong Le·Van-Phuc Hoang
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Abstract

Understanding medical texts presents significant challenges due to complex terminology and context-specific language. This paper introduces Medalyze, an AI-powered application designed to enhance the comprehension of medical texts using three specialized FLAN-T5-Large models. These models are fine-tuned for (1) summarizing medical reports, (2) extracting health issues from patient-doctor conversations, and (3) identifying the key question in a passage. Medalyze is deployed across a web and mobile platform with real-time inference, leveraging scalable API and YugabyteDB. Experimental evaluations demonstrate the system's superior summarization performance over GPT-4 in domain-specific tasks, based on metrics like BLEU, ROUGE-L, BERTScore, and SpaCy Similarity. Medalyze provides a practical, privacy-preserving, and lightweight solution for improving information accessibility in healthcare.

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Van-Tinh Nguyen, Hoang-Duong Pham, Thanh-Hai To, Cong-Tuan Hung Do, Thi-Thu-Trang Dong, Vu-Trung Duong Le, Van-Phuc Hoang. 2025-05-17. Medalyze: Lightweight Medical Report Summarization Application Using FLAN-T5-Large. https://arxiv.org/abs/2505.17059

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