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

arXiv 2505.17059 ↗First posted 17 May 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

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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References · 8

  1. 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 246
  2. 2024A Continued Pretrained LLM Approach for Automatic Medical Note Generationcited by 30
  3. 2024Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinationscited by 27
  4. 2024Closing the gap between open-source and commercial large language models for medical evidence summarizationcited by 21
  5. 2022DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processingcited by 14
  6. 2023Harnessing ChatGPT for thematic analysis: Are we ready?cited by 8
  7. 2024CACER: Clinical Concept Annotations for Cancer Events and Relationscited by 2
  8. 2025MedCT: A Clinical Terminology Graph for Generative AI Applications in Healthcarecited by 1

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