Scalable Scientific Interest Profiling Using Large Language Models.

ABSTRACT:

Objective

Research profiles highlight scientists' research focus, enabling talent discovery and fostering collaborations, but they are often outdated. Automated, scalable methods are urgently needed to keep these profiles current.

Methods

In this study, we design and evaluate two Large Language Models (LLMs)-based methods to generate scientific interest profiles-one summarizing researchers' PubMed abstracts and the other generating a summary using their publications' Medical Subject Headings (MeSH) terms-and compare these machine-generated profiles with researchers' self-summarized interests. We collected the titles, MeSH terms, and abstracts of PubMed publications for 595 faculty members affiliated with Columbia University Irving Medical Center (CUIMC), for 167 of whom we o

SUBMITTER: Liang Y 

PROVIDER: S-EPMC12393255 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature