NEW SPEAKER/NEW TIME - Helping physicians make sense of medical evidence with Large Language Models
Speaker
Byron Wallace
Northeastern University in the Khoury College of Computer Sciences
Host
Marzyeh Ghassemi
IMES, CSAIL, EECS
Abstract:
Decisions about patient care should be supported by data. But much clinical evidence—from notes in electronic health records to published reports of clinical trials—is stored as unstructured text and so not readily accessible. The body of such unstructured evidence is vast and continues to grow at breakneck pace, overwhelming healthcare providers and ultimately limiting the extent to which patient care is informed by the totality of relevant data. NLP methods, particularly large language models (LLMs), offer a potential means of helping domain experts make better use of such data, and ultimately to improve patient care.
In this talk I will discuss recent and ongoing work on designing and evaluating LLMs as tools to assist physicians and other domain experts navigate and making sense of unstructured biomedical evidence. These efforts suggest the potential of LLMs as an interface to unstructured evidence. But they also highlight key challenges—not least of which is ensuring that LLM outputs are factually accurate and faithful to source material.
Bio:
Byron Wallace is the Sy and Laurie Sternberg Interdisciplinary Associate Professor and Director of the BS in Data Science program at Northeastern University in the Khoury College of Computer Sciences. His research is primarily in natural language processing (NLP) methods, with an emphasis on their application in healthcare and the challenges inherent to this domain.
Decisions about patient care should be supported by data. But much clinical evidence—from notes in electronic health records to published reports of clinical trials—is stored as unstructured text and so not readily accessible. The body of such unstructured evidence is vast and continues to grow at breakneck pace, overwhelming healthcare providers and ultimately limiting the extent to which patient care is informed by the totality of relevant data. NLP methods, particularly large language models (LLMs), offer a potential means of helping domain experts make better use of such data, and ultimately to improve patient care.
In this talk I will discuss recent and ongoing work on designing and evaluating LLMs as tools to assist physicians and other domain experts navigate and making sense of unstructured biomedical evidence. These efforts suggest the potential of LLMs as an interface to unstructured evidence. But they also highlight key challenges—not least of which is ensuring that LLM outputs are factually accurate and faithful to source material.
Bio:
Byron Wallace is the Sy and Laurie Sternberg Interdisciplinary Associate Professor and Director of the BS in Data Science program at Northeastern University in the Khoury College of Computer Sciences. His research is primarily in natural language processing (NLP) methods, with an emphasis on their application in healthcare and the challenges inherent to this domain.