3 citations · 3 across the 4 of their papers we have counts for
8 papers
Closing the gap between open-source and commercial large language models for medical evidence summarization
Gongbo Zhang, Qiao Jin, Yiliang Zhou +11
Large language models (LLMs) hold great promise in summarizing medical evidence. Most recent studies focus on the application of proprietary LLMs. Using proprietary LLMs introduces…
Shadow and Light: Digitally Reconstructed Radiographs for Disease Classification
Benjamin Hou, Qingqing Zhu, Tejas Sudarshan Mathai +3
In this paper, we introduce DRR-RATE, a large-scale synthetic chest X-ray dataset derived from the recently released CT-RATE dataset. DRR-RATE comprises of 50,188 frontal Digitally…
Benchmarking Retrieval-Augmented Generation for Medicine
Guangzhi Xiong, Qiao Jin, Zhiyong Lu +1
While large language models (LLMs) have achieved state-of-the-art performance on a wide range of medical question answering (QA) tasks, they still face challenges with hallucinatio…
Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports
Qingqing Zhu, Xiuying Chen, Qiao Jin +6
In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging. Current metrics,…
Quality of Answers of Generative Large Language Models vs Peer Patients for Interpreting Lab Test Results for Lay Patients: Evaluation Study
Zhe He, Balu Bhasuran, Qiao Jin +6
Lab results are often confusing and hard to understand. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to get their questions answered. We…
PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge
Chih-Hsuan Wei, Alexis Allot, Po-Ting Lai +7
PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for…