A No-Code, Guideline-Based Custom GPT Outperforms Cardiologists in Response Quality for Cardiac Amyloidosis

Abstract

Background

Cardiac amyloidosis (CA) is increasingly recognized in clinical practice. Whether a guideline-based large language model can deliver clinician-level answer quality for CA remains unknown.

Objectives

This study aimed to develop a guideline-based custom generative pretrained transformer (GPT) (AmyloGPT) and evaluate whether its response quality matches or exceeds that of board-certified cardiologists for questions regarding CA.