Leveraging Machine Learning in Organ Transplantation: Optimizing Donor Matching and Forecasting Post-Transplant Recovery

Abstract

For individuals whose organs have failed, organ transplantation is clinically necessary due to the survival benefits. To increase transplant success rates, it is still crucial to improve donor-recipient matching and post-transplant outcome prediction. In order to increase organ transplantation success rates, this research assesses how machine learning technology creates organ donor matching systems and predicts transplant outcomes.

Methods: In order to conduct a comprehensive literature evaluation, this study gathered data from the IEEE Xplore, Scopus, and PubMed databases between 2018 and 2024. Articles that examine how machine learning (ML) aids in donor matching, forecasts transplant results, and incorporates AI capabilities into organ transplantation operations are included in the analysis.

Results: ML and AI By analysing large datasets, including as genetic snapshots, immunological characteristics, and clinical information, deep learning models and other machine learning algorithms show exceptional efficacy in donor-recipient matching. In order to help patients prevent organ rejection and improve transplant durability, these prediction models have shown success in improving transplant outcome assessment. AI-powered post-transplant care systems enhance patient outcomes by predicting problems and tracking the healing process in real time.

Conclusion: Artificial intelligence technologies have improved organ transplant outcomes and revolutionised donor matching procedures. By removing current obstacles to data integration and quality, future developments in AI and ML technologies will increase the effectiveness of organ transplantation. Future research should focus on creating predictive models while addressing ethical concerns related to the use of AI in healthcare systems.