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

2025/05/17 by Nguyen, Van-Tinh, Pham, Hoang-Duong, To, Thanh-Hai +4
#Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2505.17059

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.

Citations

• MedCT: A Clinical Terminology Graph for Generative AI Applications in Healthcare
• Closing the gap between open-source and commercial large language models for medical evidence summarization
• A Continued Pretrained LLM Approach for Automatic Medical Note Generation
• Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinations
• Adapted large language models can outperform medical experts in clinical text summarization
• Using LLM for Real-Time Transcription and Summarization of Doctor-Patient Interactions into ePuskesmas in Indonesia: A Proof-of-Concept Study

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