Interdisciplinary Development and Fine-Tuning of CARDIO, a Large Language Model for Cardiovascular Health Education in HIV Care: Tutorial

作者：Ryan Rullo, Ali Maatouk, Tinglin Huang, Jialin Chen, Weikang Qiu, Giselle O’Connor, Julie A. Womack, Tatiana Sadak, Christine Rodriguez, Pedro Carneiro, Tania de Jesús Espinosa, Ami Marshall, Rex Ying, S. Raquel Ramos · 发表于：Journal of Medical Internet Research · 年份：2025 · DOI：10.2196/77053 · 被引用次数：5 · 研究领域：HIV-related health complications and treatments、Artificial Intelligence in Healthcare and Education、Machine Learning in Healthcare

BACKGROUND: The integration of artificial intelligence in health care presents a significant opportunity to revolutionize patient care. In the United States, an estimated 129 million people have at least 1 chronic illness, with 42% having 2 or more. Despite being largely preventable, the prevalence of chronic illness is expected to rise and impose significant economic burdens and financial toxicity on health care consumers. OBJECTIVE: We leveraged an interdisciplinary team encompassing nursing, public health, and computer science to optimize health through prevention education for cardiovascular and metabolic comorbidities in persons living with HIV. In this tutorial, we describe the iterative, data-based development and evaluation of an intersectionality-informed large language model designed to support patient teaching in this population. METHODS: First, we curated data by scraping publicly available, authoritative, evidence-based sources to capture a comprehensive dataset, supplemented by publicly available HIV forum content. Second, we benchmarked candidate large language models and generated a fine-tuning dataset using GPT-4 through multiturn question and answer conversations, using standardized metrics to assess baseline model performance. Third, we iteratively refined the selected model via low-rank adaptation and reinforcement learning, integrating quantitative metrics with qualitative expert evaluations. RESULTS: Pre-existing large language models (LLMs) demonstrated...