Publications by authors named "Lance Dzubinski"
Background: Recent studies, including those by the National Board of Medical Examiners, have highlighted the remarkable capabilities of recent large language models (LLMs) such as ChatGPT in passing the United States Medical Licensing Examination (USMLE). However, there is a gap in detailed analysis of LLM performance in specific medical content areas, thus limiting an assessment of their potential utility in medical education.
Objective: This study aimed to assess and compare the accuracy of successive ChatGPT versions (GPT-3.
Background: Recent studies, including those by the National Board of Medical Examiners (NBME), have highlighted the remarkable capabilities of recent large language models (LLMs) such as ChatGPT in passing the United States Medical Licensing Examination (USMLE). However, there is a gap in detailed analysis of these models' performance in specific medical content areas, thus limiting an assessment of their potential utility for medical education.
Objective: To assess and compare the accuracy of successive ChatGPT versions (GPT-3.
Article Synopsis
- The study evaluated how well a machine learning model can predict serious complications (like stroke or death) in patients undergoing hemiarch surgery, using data from 602 patients treated between 2009 and 2022.
- The researchers created models using various patient demographics and surgical details, achieving an impressive accuracy rate of 88% in identifying those at risk for life-altering events.
- The findings suggest that this machine learning approach could enhance risk assessment for patients and aid doctors in making better clinical decisions.