Addressing artificial intelligence gaps in transplant medecine: a machine learning solution

Abstract

Introduction: Artificial intelligence (AI) shows promise in transplant medicine, particularly in organ matching and rejection prediction. However, gaps remain in personalized immunosuppression, including optimal drug dosing, patient-specific data integration, and clinical implementation. This study identifies these gaps and proposes a machine learning model to optimize immunosuppressive therapy. Material and Methods: A systematic review was conducted using PubMed, Scopus, and Web of Science from 2010 to 2024 with keywords: "Artificial Intelligence," "Transplant Medicine," "Rejection Prediction," and "Patient Care Optimization." Studies discussing AI applications in rejection prediction or patient care in organ transplantation were included. Data on study design, AI methods, outcomes, and limitations were extracted. Findings show most AI models rely on static predictors and fail to adapt to real-time changes like infections and inflammation. Multi-omics data, crucial for drug metabolism and immune response, are rarely integrated, reducing accuracy. Generalizability is also limited, as most models are trained on small, single-center datasets further reducing accuracy. To address these gaps, we propose a machine learning model using longitudinal transplant data. It will integrate electronic health records, pharmacokinetics, genomics, and biomarkers to predict individualized dosing. Recurrent neural networks or transformer-based architectures will update recommendations based on patient-specific responses. Model performance will be validated using real-world clinical data and benchmarked against traditional dosing protocols. Results: The review included 68 articles, with 14 meeting inclusion criteria. While AI has been applied to organ matching and rejection prediction, no existing models provide real-time, patient-specific immunosuppression adjustments, impacting patient outcomes. The proposed model aims to bridge this gap, potentially reducing rejection rates and improving outcomes. Conclusions: This study identifies deficiencies in AI-driven immunosuppression management, particularly in real-time dose adjustments, multi-omics integration, and model generalizability. The proposed machine learning framework seeks to create an adaptive, personalized dosing system to enhance transplant outcomes and minimize rejection risks.