AI-Driven Prediction and Prevention of Primary Graft Dysfunction After Heart Transplantation

ABSTRACT: Primary graft dysfunction (PGD), defined as ventricular dysfunction occurring within 24 h following heart transplantation, is a life-threatening complication following heart transplantation. It affects approximately 8–10% of recipients and decreases the 1-year survival from 95% to 75%. The convergence of machine-based learning, multi-omics biomarker discovery, and advanced organ preservation technologies provides a unique opportunity to transform PGD from poorly predictable into a computable, potentially preventable complication. This focused review combines the rapidly evolving evidence at the intersection of artificial intelligence (AI) and PGD biology, offering a novel conceptual framework, which we named the “AI-enhanced PGD prevention pipeline”. This algorithm maps computational tools to every transplant step: donor evaluation, organ preservation and transportation, intraoperative management, and early postoperative surveillance. We critically appraise the evolution from traditional scoring systems (RADIAL, PREDICTA) through contemporary machine learning (ML) models, examine the integration of emerging molecular biomarkers into computational architectures, and evaluate how AI-guided organ preservation platforms may close the loop between prediction and prevention. Moreover, we propose a research agenda to advance this field toward prospective validation and clinical deployment.