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

arXiv: https://arxiv.org/abs/2505.17059 · HTML: https://mlsanity.com/paper/2505.17059/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 17 May 2025.

## Builds on (most cited first, 8 of 8)

- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [A Continued Pretrained LLM Approach for Automatic Medical Note Generation](https://mlsanity.com/paper/2403.09057/) (2024)
- [Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinations](https://mlsanity.com/paper/2402.07023/) (2024)
- [Closing the gap between open-source and commercial large language models for medical evidence summarization](https://mlsanity.com/paper/2408.00588/) (2024)
- [DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing](https://mlsanity.com/paper/2209.14901/) (2022)
- [Harnessing ChatGPT for thematic analysis: Are we ready?](https://mlsanity.com/paper/2310.14545/) (2023)
- [CACER: Clinical Concept Annotations for Cancer Events and Relations](https://mlsanity.com/paper/2409.03905/) (2024)
- [MedCT: A Clinical Terminology Graph for Generative AI Applications in Healthcare](https://mlsanity.com/paper/2501.06465/) (2025)

## 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.