Automatic Personalized Impression Generation for PET Reports Using Large Language Models.

ABSTRACT:

Purpose

To determine if fine-tuned large language models (LLMs) can generate accurate, personalized impressions for whole-body PET reports.

Materials and methods

Twelve language models were trained on a corpus of PET reports using the teacher-forcing algorithm, with the report findings as input and the clinical impressions as reference. An extra input token encodes the reading physician's identity, allowing models to learn physician-specific reporting styles. Our corpus comprised 37,370 retrospective PET reports collected from our institution between 2010 and 2022. To identify the best LLM, 30 evaluation metrics were benchmarked against quality scores from two nuclear medicine (NM) physicians, with the most aligned metrics selecting the model for expert evaluation. In a subs

SUBMITTER: Tie X 

PROVIDER: S-EPMC10614982 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature