Efficacy of a large language model in classifying branch-duct intraductal papillary mucinous neoplasms.

ABSTRACT:

Objectives

Appropriate categorization based on magnetic resonance imaging (MRI) findings is important for managing intraductal papillary mucinous neoplasms (IPMNs). In this study, a large language model (LLM) that classifies IPMNs based on MRI findings was developed, and its performance was compared with that of less experienced human readers.

Methods

The medical image management and processing systems of our hospital were searched to identify MRI reports of branch-duct IPMNs (BD-IPMNs). They were assigned to the training, validation, and testing datasets in chronological order. The model was trained on the training dataset, and the best-performing model on the validation dataset was evaluated on the test dataset. Furthermore, two radiology residents (Readers 1 and 2) and an

SUBMITTER: Sato M 

PROVIDER: S-EPMC12830448 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature