Design and Architecture of a Generative-AI-Supported, Nonphysician-Delivered Model for GDMT Optimization in HFrEF The ASSIST-HF Trial

Patients with heart failure with reduced ejection fraction require rapid initiation and uptitration of guideline-directed medical therapy (GDMT), which is resource-intensive. In a prospective, open-label pilot trial, we assessed the feasibility, acceptability, and safety of a generative artificial intelligence–powered virtual assistant (VA), with retrieval-augmented generation and expert prompt engineering, to optimize GDMT. Patients with new heart failure with reduced ejection fraction (n = 60) were randomized to VA-guided care, delivered by nonmedical staff at 2-weekly intervals or standard-of-care treatment delivered by doctors or nurses. At 12 weeks, patients in the VA arm had superior GDMT optimization across all medication classes, lower N-terminal pro–B-type natriuretic peptide, and fewer hospitalizations. Patient-reported acceptability, appropriateness and feasibility scores were high, with no safety disagreements between VA and clinician recommendations. Treatment by an artificial intelligence–powered VA, run by nonmedical staff, with minimal remote medical supervision, is acceptable to patients, and can safely and effectively optimize GDMT, representing a scalable strategy to optimize treatment and health care resource utilization.