AI-enhanced Liver Transplant: A Neural Network cradling a stylized liver, symbolizing the use of AI in organ transplantation.

Smarter Liver Transplants: How AI Could Save Hundreds of Lives

Mira Elwood in Health & Wellness June 2025 • 4 min read.

"An innovative AI model promises to improve liver transplant allocation, offering a more equitable and efficient system that could save hundreds of lives each year."

For individuals facing end-stage liver disease, a transplant can be a life-saving intervention. However, the ongoing discrepancy between the number of available organs and the number of patients awaiting transplantation creates a critical need for a system that can fairly and accurately prioritize those most in need. For years, the Model for End-Stage Liver Disease (MELD) has been used to rank transplant candidates, but it has limitations.

Traditional methods, including MELD, often rely on a limited set of lab values and may not fully capture the complex reality of a patient's condition. This can lead to inequities, where some patients are either disadvantaged or receive 'artificial' prioritization through exception points. The challenge lies in creating a system that is both objective and capable of adapting to the nuances of individual cases.

Now, a new approach is emerging: artificial intelligence. An AI model called Optimized Prediction of Mortality (OPOM) is poised to transform liver transplant allocation. This model, harnessing machine learning, offers a more dynamic and precise method for predicting a candidate's likelihood of survival, promising a fairer and more efficient allocation process.

How Does AI Improve Transplant Allocation?

AI-enhanced Liver Transplant: A Neural Network cradling a stylized liver, symbolizing the use of AI in organ transplantation.

OPOM utilizes a sophisticated approach to assess a candidate's suitability for liver transplantation. It's built on machine learning Optimal Classification Tree models, trained to predict a candidate's three-month waitlist mortality or removal, using the Standard Transplant Analysis and Research (STAR) dataset. This tool aims to predict the probability that a patient will either die or become unsuitable for a liver transplant within three months, considering their individual characteristics.

Unlike traditional methods that depend on a limited number of lab values, OPOM incorporates a broader range of variables, refining risk assessments with data-driven insights. By considering multiple data points, including the trajectories of lab values, the model offers a holistic and adaptive perspective.

More Accurate Predictions: OPOM delivers a substantially higher AUC (Area Under the Curve) across all disease severity groups, indicating improved accuracy in predicting mortality.
Equitable Prioritization: By objectively prioritizing candidates based on disease severity, OPOM promotes a fairer distribution of available livers.
Potential for Lives Saved: Simulations suggest that OPOM could significantly reduce mortality among transplant candidates, potentially saving hundreds of additional lives annually.

In a study comparing OPOM to MELD, OPOM allocation reduced mortality on average by 417.96 (406.8-428.4) deaths every year in LSAM analysis. This survival increase was noted across all demographics, diagnoses and regions. The development and validation of OPOM represents a crucial step toward a more equitable and effective liver transplant system.

The Future of Liver Transplants is Data-Driven

The implementation of AI in liver transplant allocation signals a broader shift towards data-driven decision-making in healthcare. The ability of machine learning to analyze complex datasets and provide nuanced predictions has the potential to revolutionize various aspects of medical practice.

However, it’s important to address existing disparities in liver distribution and consider broader access to care. AI tools like OPOM should be continually refined and adapted to ensure maximum benefit for all patient populations, regardless of geographic location or socioeconomic status.

As AI continues to evolve, its integration into transplant medicine holds great promise. OPOM not only improves liver transplant allocation, but paves the way for using machine learning to guide clinical practice, potentially influencing national policy and, above all, saving lives.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1111/ajt.15172, Alternate LINK

Title: Development And Validation Of An Optimized Prediction Of Mortality For Candidates Awaiting Liver Transplantation

Subject: Pharmacology (medical)

Journal: American Journal of Transplantation

Publisher: Wiley

Authors: Dimitris Bertsimas, Jerry Kung, Nikolaos Trichakis, Yuchen Wang, Ryutaro Hirose, Parsia A. Vagefi

Published: 2018-12-06

Everything You Need To Know

What is the Model for End-Stage Liver Disease (MELD), and why is it important in this context?

The Model for End-Stage Liver Disease (MELD) has been the primary method for prioritizing patients awaiting liver transplants. However, it has limitations because it uses a limited set of lab values and may not fully capture the complexities of a patient's condition. This can lead to unfairness, with some patients being disadvantaged or receiving 'artificial' prioritization through exception points. Its significance lies in its established role in the liver transplant process before the advent of AI-driven models like Optimized Prediction of Mortality (OPOM).

What is Optimized Prediction of Mortality (OPOM), and why is it important?

Optimized Prediction of Mortality (OPOM) is an artificial intelligence model designed to improve the allocation of livers for transplant. It utilizes machine learning to predict a transplant candidate's likelihood of survival, offering a more dynamic and precise method than traditional approaches. Its importance stems from its ability to provide a more data-driven approach, considering a wider range of variables compared to the Model for End-Stage Liver Disease (MELD), thereby improving fairness and efficiency in the allocation process. The implications of using OPOM include more accurate predictions of patient outcomes, equitable prioritization based on disease severity, and the potential to save more lives by ensuring that the sickest patients receive transplants in a timely manner.

What are the machine learning Optimal Classification Tree models, and how do they work within OPOM?

The machine learning Optimal Classification Tree models are the core of Optimized Prediction of Mortality (OPOM). These models are trained using the Standard Transplant Analysis and Research (STAR) dataset to predict a candidate's three-month waitlist mortality or removal. The models consider a broader range of variables, including the trajectories of lab values. This holistic approach allows for more accurate risk assessments compared to the Model for End-Stage Liver Disease (MELD). The implications are that they provide more nuanced and precise predictions, leading to better allocation decisions and improved patient outcomes.

How does OPOM improve upon the Model for End-Stage Liver Disease (MELD)?

OPOM's advantage over the Model for End-Stage Liver Disease (MELD) lies in its ability to provide more accurate predictions of patient mortality. By incorporating a wider array of data points and leveraging machine learning, OPOM achieves a higher Area Under the Curve (AUC) across all disease severity groups, indicating better predictive accuracy. This improved accuracy ensures that transplant decisions are based on a more comprehensive assessment of a patient's condition, leading to more equitable prioritization and potentially saving more lives. The use of multiple data points, including trajectories of lab values, gives it a more adaptive perspective compared to the Model for End-Stage Liver Disease (MELD).

What are the potential benefits of using OPOM in liver transplant allocation?

The implementation of Optimized Prediction of Mortality (OPOM) can lead to several benefits in liver transplant allocation. The simulations indicate that OPOM could significantly reduce mortality among transplant candidates, potentially saving hundreds of additional lives annually. It ensures that the sickest patients are prioritized, promoting a fairer distribution of available livers and reduces the number of deaths compared to the Model for End-Stage Liver Disease (MELD). The implications extend to a broader shift towards data-driven decision-making in healthcare, with machine learning revolutionizing various aspects of medical practice by providing more nuanced and accurate predictions.