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.
@article{arxiv.2505.17059,
title = {Medalyze: Lightweight Medical Report Summarization Application Using FLAN-T5-Large},
author = {Van-Tinh Nguyen and Hoang-Duong Pham and Thanh-Hai To and Cong-Tuan Hung Do and Thi-Thu-Trang Dong and Vu-Trung Duong Le and Van-Phuc Hoang},
journal= {arXiv preprint arXiv:2505.17059},
year = {2025}
}
Comments
12 pages, 8 figures. Submitted to IEEE Access for review. Preliminary version posted for early dissemination and feedback