Jordan G. Nestor

Sep 8, 2024 | nature.com | Qiao Jin |Yiliang Zhou |Song Wang |Jordan G. Nestor |Ali Soroush |Zhiyong Lu | +1 more

AbstractLarge language models (LLMs) hold great promise in summarizing medical evidence. Most recent studies focus on the application of proprietary LLMs. Using proprietary LLMs introduces multiple risk factors, including a lack of transparency and vendor dependency. While open-source LLMs allow better transparency and customization, their performance falls short compared to the proprietary ones.

Dec 13, 2023 | nature.com | Atlas Khan |Ning Shang |Jordan G. Nestor |Chunhua Weng |Ali Gharavi

AbstractChronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability.

Aug 24, 2023 | nature.com | Zhaoyi Sun |Jordan G. Nestor |Ali Soroush |Pierre Elias |Justin F. Rousseau |Chunhua Weng | +1 more

AbstractRecent advances in large language models (LLMs) have demonstrated remarkable successes in zero- and few-shot performance on various downstream tasks, paving the way for applications in high-stakes domains. In this study, we systematically examine the capabilities and limitations of LLMs, specifically GPT-3.5 and ChatGPT, in performing zero-shot medical evidence summarization across six clinical domains.