AI in Heart Transplants: How U.S. Hospitals Are Saving More Lives

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AI is transforming heart transplants with smarter donor matching, early rejection detection, and improved patient survival.

AI technology helping doctors match donor hearts with patients in U.S. hospitals

Imagine, years of waiting, and then, finally there is a chance of getting a new heart tuned to your specifications by a clever computer program. That is the practical future of artificial intelligence (AI) in transplant medicine today.

State-of-the-art hospitals in the United States are using AI to enhance all parts of the heart transplantation process, matching donor hearts with needy patients, forecasting complications prior to their occurrence. These developments are making what science fiction used to feel become a new life for patients. To understand how AI is transforming cardiology beyond transplants, AI in Cardiology: How Artificial Intelligence Is Transforming Heart Care

As an example, at the Mayo Clinic in Minnesota, the AI-guided methodology helped a 17-year old Kataliya who had a reoccurring rare antibody profile finally receive the life-saving heart she needed. It is with the hope of such a partnership between doctors and algorithms that lives are already being saved, and that the future of such a partnership will only grow stronger.

AI in Donor Heart Matching

A heart transplant patient at Mayo Clinic holds a heart-shaped pillow, symbolizing the life-saving impact of AI-assisted transplants.

Finding a suitable heart donor and an appropriate recipient is one of the most difficult tasks in heart transplant. Classically, doctors go by blood type above size matches, which are like finding a needle in a haystack. Here, AI can be that potent matchmaker. In one inspiring story, teenager Kataliya had a 97% likelihood of rejecting just about any donor heart due to her antibodies. (Source: Mayo Clinic Magazine)

Mayo Clinic transplant cardiologist Dr. Rohan Goswami explains how AI was used in her case. It would be able to add a new level of understanding to her medical team about her risks and the extent of her potential success in being successfully matched. In other words, AI sifted through enormous patient data and was able to find a single heart in a million that Kataliya could accept. Kataliya can now run and finally live life out loud after, on June 6, 2023, she received that AI-matched heart.

AI is also quickening the matching procedure, and this can especially be essential when one does not have much time to spare. It can usually take medical professionals 30 minutes or more to determine the volume and compatibility of a donor heart in a life-or-death emergency. Cincinnati Children’s Hospital engineers demonstrated that a deep-learning AI model could analyze the same thing in seconds. As a matter of fact, their AI system measured the fitness of donor hearts in 94.5% accurate results as done manually. (Source: Materialise.com)

According to Dr. David L. S. Morales of Cincinnati Children’s Hospital, through AI, it will be possible to advance the number of donors with acceptable hearts to the patients on the waiting list, thus serving to enhance survival rates among the patients in the waiting list. AI can be described shortly as expanding the donor pool, organs that could not previously be used can be used confidently and available patients can get transplant quicker.

AI Predicting Heart Transplant Rejection

The greatest risk after getting a heart transplant is rejection where the new heart is attacked by the immune system. At the moment, physicians use biopsy slides of heart tissues to search for early indicators. This practice may be subjective and time consuming. AI is altering that. Teams at Emory University, Case Western Reserve, University of Pennsylvania, and Cleveland Clinic have trained algorithms to analyze cardiac biopsy images to much greater detail. Rather than gauging information by looking at the slides. AI can measure cell shapes, textures, and patterns, which are not visible to the naked eye.

The outcomes are quite exquisite. The study developed the Cardiac Allograft Rejection Evaluator (CARE), a machine-learning technology that was trained on the images of about 3,000 patients undergoing a heart biopsy. CARE in published studies was much more accurate in predicting risk of rejection compared with the traditional grading system. According to Emory researcher, Sara Arabyarmohammadi, the old grading was qualitative, vague and lacks diagnostic accuracy, and this resulted in either overtreatment or undertreatment.

CARE, however, measures the changes in tissue, and points out minor warning signals. In reality, their NIH-funded study showed that the CARE's predictions of severity of rejection matched with what occurred in patients much better than the ratings of a pathologist. Better still, the AI did not use some black box magic, it actually utilises user-friendly features that can be grasped by pathologists unlike non-transparent AI models. In laymen terms, the computer can see what a human eye cannot, but is able to explain his/her discoveries in terms that are understandable by a physician.

Harvard’s Brigham and Women’s Hospital has a similar story. They have constructed an automated intelligence machine known as CRANE to detect rejection. In a pilot exercise, CRANE increased convergence in diagnosis among pathologists in Boston, Switzerland, and Turkey. Doctors saw higher accuracy in readings when they used CRANE, and they reduced the time expended during the examination process as well - a key saving factor when every minute counts.

According to Dr. Faisal Mahmood, a researcher with Harvard Medicine, combining AI with human wisdom could enhance expert agreement and lessen the time requirement. In other words, the AI serves as a second pair of eyes monitoring slides in order to make sure that doctors are able to detect rejection earlier and address it more promptly.

This is viewed as a continuation of change among the clinicians. Dr. W.H. Wilson Tang, staff cardiologist and Research Director in Cleveland Clinic’s Section of Heart Failure and Cardiac Transplantation, suggests that AI can better identify the pathologic characteristics and, therefore, better assess and care for transplanted patients. AI tools are already playing the vital role of alerting doctors to trouble before it strikes, thereby saving more hearts in the early years after surgery. It is not magic technology but an additional pair of eyes that is looking meticulously at all the smallest of details.

Smarter Heart Transplant Care with AI

AI is not only to match and diagnose, it is also helping to transform post-transplant care. In the historic trends, heart transplant patients have to undergo regular biopsies and a generalized immunosuppression protocol. Today, AI is being used by researchers who can personalize follow-ups.

For example, in one study, doctors at the Mayo Clinic have published research indicating up to 85% accuracy in predicting mild rejection using artificial intelligence-based analysis of a routine ECG (electrocardiogram). This reduces the number of the painful biopsy procedures to be faced by patients who are low risk. Other teams are constructing AI models that monitor vitals and labs of their patients and recommend the just right amount of anti-rejection medication. Consider a scenario where the AI is used to constantly optimize your drug regimen to minimize the amount of each drug to reduce side effects.

The other breakthrough consists of keeping the hearts of donors alive outside the body for longer durations. Doctors are now in a position to employ the use of AI in determining the hearts to place on a perfusion machine (ex‐vivo pump) in order to heal them. There are early indications that even assessing the health of an organ can be done in real time by AI. Mayo Clinic specialists believe that AI will allow finding more usable donor hearts and reduce the waste of organs. In practice, this may imply a borderline quality heart discarded may undergo a freshening up and may safely be transplanted due to intelligent monitoring.

AI and Doctors: A Collaborative Approach

The similarity is that AI is not a system that replaces but rather a system that collaborates. As top researchers have pointed out, such devices are intended to supplement, not substitute, doctors. As Dr. Kenneth B. Margulies of Penn Medicine, the lead researcher on an NIH-funded project, has described it: The agenda is a system of computer-aided tissue diagnostics that helps pathologists identify characteristics more quickly and accurately.

From Cleveland Clinic, Dr. Jerry Estep points out that AI can place providers in a position to provide earlier and improved treatment, to the benefit of outcomes. In practice, a pathologist may find a biopsy slide and rely upon the AI to highlight any issues that the pathologist has missed. Physicians have the last say, however, they do so with much more accurate information.

The cooperative approach is proven by research conducted across the country. Numerous researchers have found that the AI programs frequently perform as accurately as human beings or even better. However, physicians require a knowledge of the reasoning behind every AI recommendation. That is why teams are working on explainable AI models that indicate particular cells or patterns that activated an alert. When the physicians observe the same clues as that of the computer, then the confidence builds. Traditionally, the ability to diagnose has been highly subjective, as one expert put it, but AI is reversing that by bringing data-driven objectivity to the diagnostic picture.

The Future of AI in Heart Transplants

Innovation goes at a thrilling speed. In recent years alone, AI tools introduced in American hospitals would have been unthinkable a decade before. The Mayo Clinic transplant surgeon, Dr. Mark D. Stegall formulates the feeling: AI will become a significant decision-making tool in the practice of physicians because it has a potential to enhance better outcomes and save lives. Even outside of transplants, Mayo researchers are pursuing AI to be able to diagnose heart disease with enough precision and in time to implement treatment procedures to prevent transplants altogether.

In conclusion on the promise, Dr. Rohan M. Goswami of Mayo Clinic states that AI has the potential to transform the whole area through better organ matching, predicting the high versus low risk, and earlier warning of a problem. They are employing AI to redefine what we know when dealing with chronic diseases, possibly preventing organ replacement therapy in that case.

As Kataliya wonders, it is already giving transplant patients a second life chance. The hope that doctors and patients may have is actually true, armed with AI in their arsenal, transplant teams throughout the country are indeed rescuing more hearts-and lives-one algorithm at a time.

AI in Heart Transplants: Giving Patients a Second Chance at Life

Cutting-edge AI technology is becoming one of the close companions of heart transplant centers. These technologies are used in research labs at Emory, Penn and Harvard, and in operating rooms at Mayo and Cleveland Clinic to mitigate rejection, improve donor matches, and provide personal care to patients. That is real hope to patients and families that circulate the ordeal of heart failure. The advancement of transplantation is clearer and heartier, given AI.


The latest updates on the innovations in the field of AI Heart Transplantation

AI has been rapidly advancing in heart transplantation, and 2026 has seen a few exciting advancements.

AI-ECG Heart Age predicts risk of rejection.

Scientists at Mayo Clinic introduced a new AI-based ECG technique that they explained compares the heart's AI prediction of biological age with the donor's age. The risk of rejection was significantly higher within 1 year of transplantation in patients with a small age difference. This easy-to-obtain, noninvasive ECG parameter may be useful to guide doctors to tailor immunosuppressive treatment and avoid avoidable biopsies.

An AI system enhances the chances of donor organ recovery by predicting donor organ viability before recovery.

Researchers at UCLA Health created a machine learning model to predict if hearts from Donation after Circulatory Death (DCD) donors will be fit for transplantation. The system might result in fewer wasted donor hearts and more successful transplants.

AI Extends Non-Invasive Rejection Monitoring

The extensive 2026 review identified AI systems integrating donor-derived cell-free DNA, gene-expression profiling, extracellular vesicles, and digital pathology for the early detection of rejection and minimising the need for invasive biopsies. These tools are potentially useful on a routine basis, but should be evaluated in future multicenter studies.

National Heart Transplant AI Research Network

Based on the success of this network in 2026, the American Heart Association established a national research network of 15 top centers to take heart transplant care to the next level with advanced analytics, AI and shared research infrastructure. The purpose of the initiative is to advance innovation and enable better patient care in the United States.


You can find a list of frequently asked questions (FAQs) here:

1. How does AI help in heart transplantation?

In the medical field, AI can aid doctors in better donor/recipient matches, predicting transplant rejection, analysing biopsy images, monitoring patient recovery and supporting personalised treatment decisions.

2. Is it possible for the transplant surgeon to be replaced by AI?

AI's role is not to replace the transplant surgeon's role in making clinical decisions, but rather to assist in these decisions.

3. Is there a decrease in the need for heart biopsies when it comes to AI?

Lower-risk patients can be identified by emerging AI technologies that analyze ECGs, blood markers and imaging. These tools can help decrease, but not eliminate, the need for routine biopsies.

4. Who is doing the best AI heart transplant research?

The following institutions in the United States are working on AI technologies for transplant: Mayo Clinic, Cleveland Clinic, Emory University, Penn Medicine, UCLA Health, Harvard/Brigham and Women's Hospital, and Cincinnati Children's Hospital.

5. What are the greatest advantages of using AI in heart transplantation?

  • Faster donor matching
  • Earlier rejection detection
  • Better survival rates
  • More personalized treatment
  • Reduced unnecessary biopsies
  • Better utilization of the donor heart.

6. What are the upcoming challenges?

There is ongoing research to enhance the sharing of data, the explainability of the AI, multicenter validation, regulatory approval, and patient privacy protection prior to widespread adoption.


References:

Include these at the end of your article.

  1. Mayo Clinic – AI-ECG Heart Age Predicts Heart Transplant Rejection (JACC 2026)
    https://www.jacc.org/doi/10.1016/j.jacc.2026.02.1840
  2. Frontiers in Cardiovascular Medicine – Artificial Intelligence Optimizes Immune Rejection Prediction and Management in Heart Transplantation (2026)
    https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1790244/full
  3. PubMed – AI in Heart Transplant Rejection Management (2026)
    https://pubmed.ncbi.nlm.nih.gov/42222122/
  4. UCLA Health – New AI Model Predicts Donor Organ Viability (2026)
    https://www.uclahealth.org/news/release/new-ai-model-can-help-predict-expiration-donors-improve
  5. American Heart Association – National Heart Transplant Research Network (2026)
    https://newsroom.heart.org/news/15-centers-selected-for-groundbreaking-research-network-to-transform-heart-transplant-care
  6. ScienceDirect – Molecular Diagnostics and AI in Heart Transplant Rejection Surveillance (2026)
    https://www.sciencedirect.com/science/article/pii/S0955470X26000339


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