Cleft lip and palate-generative pre-trained transformer: A parameter-efficient, domain-specific language model for cleft lip and palate clinical applications.

ABSTRACT:

Background

General-purpose large language models (LLMs) often lack specialized knowledge for cleft lip and palate (CLP), limiting clinical utility. Accessible, accurate, and affordable AI tools could potentially supplement existing educational resources for clinicians and patients.

Objective

To develop and evaluate CLP-generative pre-trained transformer (GPT), a parameter-efficient model fine-tuned for CLP, and to assess whether a smaller specialized model can achieve performance comparable to large generalist models for clinical deployment.

Method

We built a CLP dataset of 7815 validated Q&A pairs from medical dialogs, literature, and guidelines. Using Qwen2-7B-Instruct as the base, we applied low-rank adaptation for domain adaptation. Performance was assessed via ph

SUBMITTER: He X 

PROVIDER: S-EPMC12847651 | biostudies-literature | 2026 Jan-Dec

REPOSITORIES: biostudies-literature