Knowledge domain and frontier trends of artificial intelligence applied in solid organ transplantation: A visualization analysis

Abstract

• AI applications in Solid Organ Transplantation (SOT) are rapidly expanding, with key contributions from institutions like the University of Toronto and University of Pittsburgh, and prominent researchers such as Benedetti E and Oberholzer J. • Robotic-assisted surgery improves precision, reduces complications, and accelerates recovery in kidney, liver, pancreas, and lung transplants, enhancing patient outcomes. • AI improves early detection, diagnosis, and prediction of transplant complications, enhancing outcomes through advanced imaging, machine learning, and timely interventions. • AI enhances organ allocation and recipient matching by integrating diverse data sources, improving graft survival predictions, and ensuring fairness with explainable models, while also optimizing immunosuppressive therapy and predicting drug dosages by integrating multi-omics data to minimize adverse effects and personalize transplant care. • Practical barriers to AI adoption in SOT, including high costs, data integration challenges, and clinician resistance, can be addressed through cost-sharing, standardization, and education, while XAI and Federated Learning improve transparency, robustness, and data privacy. Solid organ transplantation (SOT) is vital for end-stage organ failure but faces challenges like organ shortage and rejection. Artificial intelligence (AI) offers potential to improve outcomes through better matching, success prediction, and automation. However, the evolution of AI in SOT research remains underexplored. This study uses bibliometric analysis to identify trends, hotspots, and key contributors in the field. 821 articles from the Web of Science Core Collection were exported for analysis. Microsoft Excel 2021 was used for descriptive statistics. VOSviewer, CiteSpace, Scimago Graphica, and Biblioshiny were used for bibliometric analysis. The ggalluvial package in R was utilized to create Sankey diagrams, and top articles were selected based on citation count. This analysis reveals the rapid expansion of AI in SOT. Key areas include robotic surgery, organ allocation, outcome prediction, immunosuppression management, and precision medicine. Robotic surgery has improved transplant outcomes. AI algorithms optimize organ matching and enhance fairness. Machine learning models predict outcomes and guide treatment, while AI-based systems advance personalized immunosuppression. AI in precision medicine, including diagnostics and imaging, is crucial for transplant success. This study highlights AI’s transformative potential in SOT, with significant contributions from countries like the USA, Canada, and the UK. Key institutions such as the University of Toronto and the University of Pittsburgh have played vital roles. However, practical challenges like ethical issues, bias, and data integration remain. Fostering international and interdisciplinary collaborations is crucial for overcoming these challenges and accelerating AI’s integration into clinical practice, ultimately improving patient outcomes.