用轻量级AI模型实现医疗报告实时摘要，提升医生阅读效率。
Medalyze: Lightweight Medical Report Summarization Application Using FLAN-T5-Large
- 基于FLAN-T5-Large微调三类专用模型，分别处理报告摘要、健康问题提取和关键问题识别。
- 在真实医疗文本上，摘要性能超越GPT-4，BLEU、ROUGE-L、BERTScore等指标更优。
- 支持网页与移动端实时推理，兼顾隐私保护与低资源部署，适合临床辅助场景。
医学文本因术语复杂和语境依赖性强，理解难度大。本文提出Medalyze，一个基于FLAN-T5-Large的轻量级AI应用，通过三个微调模型实现三项功能：(1) 医疗报告摘要，(2) 从医患对话中提取健康问题，(3) 识别段落中的核心问题。系统部署于网页与移动平台，支持实时推理，采用可扩展API与YugabyteDB。实验表明，在领域特定任务中，该系统在BLEU、ROUGE-L、BERTScore及SpaCy相似度等指标上优于GPT-4。Medalyze提供了一种实用、隐私友好且轻量化的解决方案，显著提升医疗信息可读性。
原文摘要 · Abstract (English)
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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