Summarizing Online Patient Conversations Using Generative Language Models: Experimental and Comparative Study.

ABSTRACT:

Background

Social media is acknowledged by regulatory bodies (eg, the Food and Drug Administration) as an important source of patient experience data to learn about patients' unmet needs, priorities, and preferences. However, current methods rely either on manual analysis and do not scale, or on automatic processing, yielding mainly quantitative insights. Methods that can automatically summarize texts and yield qualitative insights at scale are missing.

Objective

The objective of this study was to evaluate to what extent state-of-the-art large language models can appropriately summarize posts shared by patients in web-based forums and health communities. Specifically, the goal was to compare the performance of different language models and prompting strategies on the task of

SUBMITTER: Nair RAS 

PROVIDER: S-EPMC12038288 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature