← Back to all news
arXiv AI By Pallika Kafle, Yipeng Zhou, Guanfeng Liu, Quan Z. Sheng, Cheng-Hsin Hsu

Medical Knowledge Simplification for Patients in the Era of LLMs: A Case Study on Diabetes

Read the original on arXiv AI →

The paper presents MediClear, an LLM-based system that simplifies diabetes-related medical information for patients. By indexing public articles from Diabetes Australia, WHO, ADA, NIDDK, and AIHW in a Retrieval-Augmented Generation knowledge base, MediClear retrieves clinically grounded content and rewrites it into patient-friendly language. Evaluation shows the system consistently lowers reading levels to the recommended patient literacy range and receives high user satisfaction in a human study.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Jun 6

Evaluating the Utility of Personal Health Records in Personalized Health AI

arXiv:2605. 18937v2 Announce Type: replace Abstract: Patient-managed Personal Health Records (PHRs) promises to empower patients to better understand their health; but information in the record is complex, potentially hindering insights.

By Rory Sayres, Kejia Chen, Ayush Jain, Matthew Thompson, Jonathan Richina, Xiang Yin, Jimmy Hu, Fan Zhang, Bob Lou, Mike Sanchez, Ines Mezerreg, Meredith Schreier, Hamsa Subramaniam, I-Ching Lee, Yugang Jia, Daniel Mcduff, Yossi Matias, Avinatan Hassidim, Dale Webster, Yun Liu, Jackie Barr, Quang Duong
llmssafety
More like this →
arXiv Computation and Language
Sep 16

Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation

The paper explores how Large Language Models (LLMs) such as GPT-4o-mini, Gemini-1.5-pro, and LLaMA can be used to automatically simplify complex medical texts into plain language, a process known as Plain Language Adaptation (PLA). It evaluates different prompting strategies, fine‑tunes models with QLoRA, and incorporates Mixture‑of‑Agents (MoA) techniques to improve adaptability and robustness. The results show that LLM‑driven PLA effectively makes healthcare information more comprehensible while preserving essential content.

By Ting-Wei Chang, Hen-Hsen Huang, Hsin-Hsi Chen
llmsagentsfine-tuning
More like this →
arXiv AI
Oct 15

Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription

The paper surveys open, non‑proprietary large language models (LLMs) and AI foundation models (AIFMs) for clinical use, presenting a taxonomy of current state‑of‑the‑art models. It empirically tests open‑weight LLMs on a personalized‑prescription task, finding that they match or surpass a proprietary baseline in overall suitability accuracy, especially when combined with Retrieval‑Augmented Generation (RAG). A subjective evaluation by blinded raters shows mixed effects of RAG on perceived suitability across multiple dimensions, and the authors discuss ethical risks and the need for responsible deployment.

By Mahdi Alkaeed, Sofiat Abioye, Adnan Qayyum, Yosra Magdi Mekki, Ilhem Berrou, Mohamed Abdallah, Ala Al-Fuqaha, Muhammad Bilal, Junaid Qadir
llmsragbenchmarks
More like this →