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

Date: Friday, September 15, 2023

Time: 3:30 PM to 4:30 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: STAR (32-D463)

Event Type: Seminar

Room Description:

Host: Marzyeh Ghassemi , IMES, CSAIL, EECS

Contact: Sheila Sharbetian, 617-324-6747, sheilash@csail.mit.edu

Relevant URL:

Speaker URL: None

Speaker Photo:
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Reminders to: seminars@csail.mit.edu

Reminder Subject: TALK: Helping physicians make sense of medical evidence with Large Language Models

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.

Research Areas:
AI & Machine Learning

Impact Areas:
Health Care

See other events that are part of the Machine Learning and Health Seminar Series, Fall 2023.

Created by Sheila Sharbetian at Tuesday, August 15, 2023 at 11:34 AM.