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

• URL: http://arxiv.org/abs/2505.17059v1
• Date: Sat, 17 May 2025 07:16:58 GMT
• Title: Medalyze: Lightweight Medical Report Summarization Application Using FLAN-T5-Large
• Authors: Van-Tinh Nguyen, Hoang-Duong Pham, Thanh-Hai To, Cong-Tuan Hung Do, Thi-Thu-Trang Dong, Vu-Trung Duong Le, Van-Phuc Hoang,
• Abstract summary: Medalyze is an AI-powered application designed to enhance the comprehension of medical texts.<n>It is deployed across a web and mobile platform with real-time inference, leveraging scalable API and YugabyteDB.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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