Medalyze: Lightweight Medical Report Summarization Application Using FLAN-T5-Large
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918031657009152 |
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| author | Nguyen, Van-Tinh Pham, Hoang-Duong To, Thanh-Hai Do, Cong-Tuan Hung Dong, Thi-Thu-Trang Le, Vu-Trung Duong Hoang, Van-Phuc |
| author_facet | Nguyen, Van-Tinh Pham, Hoang-Duong To, Thanh-Hai Do, Cong-Tuan Hung Dong, Thi-Thu-Trang Le, Vu-Trung Duong Hoang, Van-Phuc |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17059 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Medalyze: Lightweight Medical Report Summarization Application Using FLAN-T5-Large Nguyen, Van-Tinh Pham, Hoang-Duong To, Thanh-Hai Do, Cong-Tuan Hung Dong, Thi-Thu-Trang Le, Vu-Trung Duong Hoang, Van-Phuc Computation and Language Artificial Intelligence 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. |
| title | Medalyze: Lightweight Medical Report Summarization Application Using FLAN-T5-Large |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.17059 |