Objectify, predict, personalize: the AI revolution in organ transplantation

#AI #Transplantation #MachineLearning #GraftRejection


Solid organ transplantation is currently the gold standard treatment for end-stage organ failure. However, numerous challenges remain, including organ shortages, complex preoperative assessments, and the risk of chronic rejection. In this context, artificial intelligence (AI)—and more specifically, machine learning (ML)—is drawing increasing interest from clinicians and researchers.

This literature review explores the latest advances in AI applied to transplantation, focusing on three major areas: pre-transplant assessment, rejection diagnosis, and personalized postoperative monitoring. The promise? To transform transplant medicine into a more predictive, objective, and personalized field.  

From segmentation to predictive scoring: AI before transplantation

Pre-transplant evaluation still often relies on subjective exams and expert judgment. To make these steps more reliable, AI models have been developed. One example: an automated kidney segmentation algorithm based on CT scan data showed near-human accuracy (Dice coefficients of 0.94 for the cortex).

In lung transplants, another algorithm using “dictionary learning” was able to predict graft viability with 85% accuracy based on CT images. Other models, such as XGBoost, can predict immediate graft function (IGF), thereby optimizing recipient selection.

An AI model based on electrocardiographic age (ECG age) also proves useful for assessing patients’ cardiovascular frailty. The greater the gap with chronological age, the higher the mortality risk—hazard ratio of 3.59 per 10-year increment.  

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Diagnosing rejection objectively

Histological diagnosis remains the cornerstone of graft rejection detection. Yet the complexity of classification systems—especially Banff—leads to problematic interobserver variability. To address this, the Banff Automation System offers an automated analysis tool integrating slides, lesion scores, and molecular data. The result? A more accurate reclassification of numerous cases and improved graft survival prediction.

Other models, such as those from Zhang’s lab or the RejectClass score, use whole-slide image analysis to anticipate graft loss. Fang et al.’s proteomics-based approach distinguished T-cell mediated rejection (TCMR) with 80% accuracy. The MMDx platform uses transcriptomic profiles to reclassify renal and even lung transplant biopsies, showing a stronger correlation with real graft survival outcomes.  

Managing immunosuppression: AI at the bedside of pharmacokinetics

Adjusting doses of cyclosporine or tacrolimus is complex and requires frequent monitoring. AI models—based on evolutionary algorithms or neural networks—can predict blood levels from clinical or genetic data. XGBoost even outperformed traditional Bayesian methods in accuracy for tacrolimus exposure prediction.  

Toward Predictive Post-Transplant Medicine

How can we anticipate complications and survival after transplantation? Models such as Random Survival Forest (RSF) allow for accurate risk stratification. In lung transplantation, this model differentiated low- and high-risk groups, with life expectancies of 52.9 months versus 14.8 months.

The ReSOLT model, developed from the UNOS database, classifies liver transplant patients into 20 prognostic subgroups, with an AUC of 0.742. Other approaches have targeted the prediction of postoperative sepsis or hepatic fibrosis using LSTM algorithms. Finally, an ensemble learning model developed in Ethiopia reached an AUC of 0.88 in predicting renal graft failure.  

A successful data transplant?

Thanks to artificial intelligence, organ transplantation is entering the era of precision. From pre-transplant evaluation to rejection management, immunosuppression, and prognosis, every step is being redesigned to be more objective, predictive, and personalized. The tools are powerful but still imperfect: data bias, heterogeneous clinical practices, and generalization limitations remain challenges to overcome.

Nevertheless, these innovations mark the beginning of a profound transformation of transplant medicine—toward greater equity, safety, and a firm commitment to precision medicine.  

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