Medalyze: Lightweight Medical Report Summarization Application Using FLAN-T5-Large
This provides a practical, privacy-preserving solution for improving information accessibility in healthcare, though it is incremental as it applies existing methods to a specific domain.
The paper tackled the challenge of understanding complex medical texts by introducing Medalyze, an AI application that uses fine-tuned FLAN-T5-Large models for summarization, extraction, and question identification, achieving superior performance over GPT-4 in domain-specific tasks based on metrics like BLEU and ROUGE-L.
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.