Search AMIA.org

Large language models (LLMs) hold immense promise for democratizing access to medical information and assisting physicians in delivering higher-quality care. However, realistic evaluations of LLMs in clinical contexts have been limited, with much focus placed on multiple-choice evaluations of clinical knowledge. In this talk, I will present a four-level framework [...]

With growing legal and scientific evidence for the importance of reducing model bias, both model developers and deployers need tools to quantify the bias. Unfortunately, algorithmic bias can take as many forms as there are implementations. In this talk, Paul M. Heider covesr a range clinical NLP use cases like [...]

Learn strategies for translating even the most technical information into compelling, human stories—so you can change the way people think, feel, and act. We’ll use sustainability communications as our lens for looking at messaging strategies that work (and those that don’t work). We’ll also talk about the buzzwords and cliches [...]

This talk will introduce the technologies powering LLMs, overview the recent prevails, and examine the mirages in the hype of LLM magic. Based on the experience in developing two clinical LLMs in the clinical domain, including GatorTron and GatorTronGPT, this talk will provide insight into the potential application of LLMs for clinical NLP and healthcare.

Acute hepatic porphyria (AHP) is a rare but treatable condition with an average diagnostic delay of 15 years. Utilizing electronic health records (EHR) data and machine learning (ML) can potentially improve the timely recognition of AHP. This study used structured and notes-based EHR data from UCSF and UCLA to develop models predicting who will be referred for AHP testing and who will test positive. The referral model achieved an F-score of 86%-91%, and the diagnosis model achieved an F-score of 92%.