The Future Role of Machine Learning in Clinical Transplantation

Katie Connor, Eoin D. O'Sullivan, Lorna P Marson, Stephen J Wigmore, Ewen M Harrison

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

The use of artificial intelligence and machine learning (ML) has revolutionised our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive data sets, exponential increases in processing power and key algorithmic developments which allow ML models to tackle increasingly challenging questions. Progressively more transplantation research is exploring the potential utility of ML models throughout the patient journey, although this has not yet widely transitioned into the clinical domain.In this review, we explore common approaches used in ML in solid organ clinical transplantation and consider opportunities for ML to help clinicians and patients. We discuss ways in which ML can aid leverage of large complex datasets, generate cutting-edge prediction models, perform clinical image analysis, discover novel markers in molecular data, and fuse datasets to generate novel insights in modern transplantation practice. We focus on key areas in transplantation where ML is driving progress, explore the future potential roles of ML and discuss the challenges and limitations of these powerful tools.
Original languageEnglish
Issue number4
Early online date18 Aug 2020
Publication statusPublished - Apr 2021


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