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

We present a data-based, step-by-step tutorial for interdisciplinary development of CARDIO, a specialized LLM, for cardiovascular health education in HIV care. Through comprehensive data curation and scraping, systematic benchmarking, and a dual-stage fine-tuning pipeline, CARDIO's performance improved markedly (accuracy 5.0, readability 4.98, professionalism 4.98, Kincaid 7.17, and Jargon 2.92). Although patient pilot testing remains forthcoming, our results demonstrate that targeted data curation, rigorous benchmarking, and iterative fine-tuning have provided a robust evaluation of the model's potential. By building an LLM tailored to cardiovascular health promotion and patient education, this work lays the foundation for innovative artificial intelligence-driven strategies to manage comorbid conditions in people living with HIV.