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.
