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

TL;DR

Medalyze is a lightweight, AI-powered medical report summarization tool that uses fine-tuned FLAN-T5-Large models to improve understanding of complex medical texts across web and mobile platforms, outperforming GPT-4 in domain-specific tasks.

Contribution

The paper introduces Medalyze, a novel application utilizing specialized FLAN-T5-Large models for medical text summarization, extraction, and question identification, with real-time deployment and superior performance.

Findings

Outperforms GPT-4 in medical summarization metrics

Provides real-time inference on web and mobile platforms

Offers a privacy-preserving, scalable healthcare information tool

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.