Professionalism/Ethics in Organ Allocation

Background

The Process

In the U.S., organ donation starts with finding a willing donor. Donors can either be alive or deceased if registered with the U.S. Organ Procurement and Transplantation Network (OPTN)[1]. After the viability of the organ(s) is determined, an algorithm matches the donor's organ(s) to recipients based on traits like blood type or donor health[2]. After matching is complete, the transplant doctors will decide to accept the organ, and quickly make arrangements for the organ delivery and setup surgery[3].

Complications

For living donors, recovery or medical issues from surgery could make returning to work or life previously difficult[4]. For recipients, medical complications such as blood loss, organ rejection, and infection are all potential risks[3]. One of the major challenges in organ donation currently is a difficulty in balancing the risk of waitlist mortality with the chances of graft and patient survival post transplant. Traditional allocation systems dont have the complexity needed to efficiently consider all the variables that go into a transplant. As a result, some candidates may be under prioritized despite having outcomes that are more favorable, while others may receive organs despite a high-risk of post transplant complications. This inefficiency highlights the need for more adaptive and data-driven tools that can personalize transplant decisions based on a broader set of patient data.

Ethical Issues

Skipping the Line[5]

Generally, organ allocation is done according to a ranking system. The ranking system takes into account the biological characteristics of the organ, and then ranks potential recipients by factors such as severity, how long they have been waiting, and likelihood of success. Usually, the offers will be made to the candidates in the order they appear on the ranking, and each doctor will have up to 1 hour to accept or reject the organ. For a long time, skipping the list was considered a last resort and resulted in an investigation by the United Network for Organ Sharing (UNOS). In recent years, however, "line-skipping" has become far more common, nearly 10 times as common as a decade ago. Rather than an offer made to a specific patient, open offers may be made to hospitals that can then choose which patient the organ goes to. The organ may still go to someone close to the top of the list, or it may go to someone thousands of spots down.

Organ allocation organizations justified this decision as a way to reduce organ waste by matching organs more quickly; however, there has not been any decrease in the number of discarded organs since this practice became more widespread. Many times patients are skipped well before the organ is in danger of being discarded. Doctors and allocation workers also allege that line-skipping may be used to increase profits for these organizations. Organs are paid for at a flat rate, so saving time and labor costs financially benefits the allocation organization. The organization with the highest line-skipping rate in the country, Lifebanc, skips the line nearly a third of the time. Most of those organs have gone to a hospital that Lifebanc hires medical advisers from, the Cleveland Clinic.

In a recent investigation, the New York Times found that at least 1,200 people had died after being skipped on the organ transplant list. Skipping the transplant list also favors wealthy white patients, exacerbating existing racial and socioeconomic disparities. UNOS has almost entirely ceased investigating these cases, only taking action in 0.5% of cases. This means organ allocation organizations are functioning with minimal oversight, and patients are experiencing harm as a result.

Instances of Discrimination

In 1999, faulty research claimed that the estimated glomerular filtration rate (eGFR), a metric for measuring kidney health, was on average higher for Black patients[6]. Due to this discrepancy, Black patients at bad eGFR levels who needed to be put on the transplant list had to wait for their condition to get worse. The National Kidney Foundation (NKF) and American Society of Nephrology (ASN) established rules for race-neutral eGFR tests in 2021[7]. A year later, OPTN forced transplant centers to adhere to similar standards[7]. In 2023, over 14,000 Black potential transplant patients were moved up the kidney wait list[6].

The risk metrics of organ transplant candidates, which help place them on the waitlist, are an area of concern. The Model for End-Stage Liver Disease (MELD) adds 1.33 points to female patients' scores, and the Kidney Donor Risk Index (KDRI) still factors in race[8]. The lung allocation score has a strong bias towards candidates in closer distances, but the composite allocation score (CAS) replaced it in 2023[8]. CAS got rid of the strict geographic constraints, but had issues with blood type bias against type O[9]. After a 3-month study, it was found that the simulation used to create CAS had an error marking type O candidates as universal recipients[9]. The policy changes to CAS caused the waiting list mortality rate and median time to transplant to be maintained or reduced for all blood types[10].

Future

How AI Can Help

Machine learning models are trained from large datasets and have the ability to consider numerous features in the set. This allows the model to create complex relationships between data from donors and recipients that are not readily available. Artificial intelligence is changing how transplant care is delivered by making it more accurate and personalized. Before a transplant, AI can improve donor-recipient matching by analyzing health and immune factors that might be too complex for traditional methods. This can reduce the chances of rejection and help ensure better outcomes. Algorithms also improve how organs are allocated by predicting which patients are most likely to benefit, not just based on urgency but on expected long-term success. AI tools can also support mental health evaluations by assessing a patient’s emotional readiness and ability to follow care plans after surgery. After the transplant, AI helps create follow-up plans tailored to each patient’s condition. It can also predict how well someone might do based on both past and current health data. By tracking patterns and spotting problems early, AI can alert doctors to signs of graft failure before symptoms show up, allowing them to act quickly and prevent complications.[11]

Pros and Cons of AI Usage

AI helps improve organ transplantation by improving matching between donors and recipients and helps to align characteristics for the most successful outcome. Current allocation systems lack the ability to prioritize in real time and continuously require updates.[12] Using AI helps combat this by ensuring the best decisions are made when they are needed.

Despite its benefits, AI in organ transplantation comes with several significant concerns. One major issue is algorithmic bias. AI models can amplify existing biases in the data they are trained on. If the training data includes disparities in race or socioeconomic status for example, the algorithm may unintentionally favor or disadvantage certain groups, leading to unfair treatment decisions. Another concern is the potential for AI to compromise human decision-making. If clinicians rely too heavily on algorithm-generated recommendations, they may overlook important context or ethical considerations that AI cannot fully address. Accountability is an issue when a doctor follows the decision-making of AI systems. There is a line between using AI as a tool and relying on it for medically significant decisions[13]. Data privacy is also a major challenge. AI systems often need access to large amounts of sensitive patient information, and without strong protections, there is a risk of misuse that could violate patient confidentiality. Additionally, many AI models, particularly deep learning systems, operate as "black boxes,"[14] meaning their internal decision-making processes are not easily interpretable. This lack of transparency can make it difficult for clinicians to understand or trust the rationale behind a recommendation and it can also make it difficult for a patient to understand what is being recommended to them as well. If patients do not fully grasp how AI is used in their care, their ability to give informed consent on life-altering procedures is negatively affected[13].

Conclusions

Organ allocation is a good case study for professional ethics, as it features exaggerated versions of the pressures that exist in most fields. To complete organ allocation ethically, employees must be able to prioritize patient good while under both time pressure and pressure from executives at hospitals and organizations.

Avoiding the pitfalls of line-skipping will require employees of hospitals and organizations to work together and prioritize patient welfare, rather than financial incentives. The New York Times investigation itself relied in part on conversations with both current employees of these organizations and employees who had left due to ethical concerns. This case also highlights the need for effective oversight organizations, and the responsibility that regulators have to investigate thoroughly and respond with appropriate action.

References

  1. ↑ "About the OPTN - OPTN". optn.transplant.hrsa.gov. Retrieved 2025-05-05.
  2. ↑ "The Organ Transplant Process | organdonor.gov". www.organdonor.gov. Retrieved 2025-05-05.
  3. ↑ a b "Organ Donation & Transplantation". Cleveland Clinic. Retrieved 2025-05-05.
  4. ↑ "Living Organ Donation | organdonor.gov". www.organdonor.gov. Retrieved 2025-05-05.
  5. ↑ Rosenthal, Brian M.; Hansen, Mark; White, Jeremy (2025-02-26). "Organ Transplant System ‘in Chaos’ as Waiting Lists Are Ignored" (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/interactive/2025/02/26/us/organ-transplants-waiting-list-skipped-patients.html. 
  6. ↑ a b "A biased test kept thousands of Black people from getting a kidney transplant. It's finally changing". AP News. 2024-04-01. Retrieved 2025-05-05.
  7. ↑ a b Lewis, Diane C.; Woods, Karima; Watkins, Tamara; Kofman, Mila; Kempf, Purvee; Winn, Joseph. "Addressing The Harmfully Slow Uptake Of Race-Neutral Kidney Function Tests". Health Affairs Forefront. doi:10.1377/forefront.20250205.468098.
  8. ↑ a b Dale, Reid; Cheng, Maggie; Pines, Katharine Casselman; Currie, Maria Elizabeth (2024-10-17). "Inconsistent values and algorithmic fairness: a review of organ allocation priority systems in the United States". BMC Medical Ethics. 25 (1). doi:10.1186/s12910-024-01116-x. ISSN 1472-6939. PMC 11483980. PMID 39420378. {{cite journal}}: Check |pmc= value (help); Check |pmid= value (help)
  9. ↑ a b Alderete, Isaac S.; Hartwig, Matthew G. (2025-02-13). "From flawed to fairer: reducing blood type bias in lung transplant allocation". American Journal of Transplantation. 0 (0). doi:10.1016/j.ajt.2025.02.003. ISSN 1600-6135. PMID 39954812. {{cite journal}}: Check |pmid= value (help)
  10. ↑ "Lung transplants for blood type O increase after modified blood type rating scale - OPTN". optn.transplant.hrsa.gov. Retrieved 2025-05-06.
  11. ↑ Bhat, Mamatha; Rabindranath, Madhumitha; Chara, Beatriz Sordi; Simonetto, Douglas A. (2023-06-01). "Artificial intelligence, machine learning, and deep learning in liver transplantation". Journal of Hepatology. Liver Transplantation. 78 (6): 1216–1233. doi:10.1016/j.jhep.2023.01.006. ISSN 0168-8278.
  12. ↑ Deshpande, Rajkiran (2024). "Smart match: revolutionizing organ allocation through artificial intelligence". Frontiers in Artificial Intelligence. 7: 1364149. doi:10.3389/frai.2024.1364149. ISSN 2624-8212. PMC 10933088. PMID 38481822. {{cite journal}}: Check |pmc= value (help); Check |pmid= value (help)
  13. ↑ a b Salybekov, Amankeldi A.; Yerkos, Ainur; Sedlmayr, Martin; Wolfien, Markus (2025-04-17). "Ethics and Algorithms to Navigate AI's Emerging Role in Organ Transplantation". Journal of Clinical Medicine. 14 (8): 2775. doi:10.3390/jcm14082775. ISSN 2077-0383. PMC 12027807. PMID 40283605 – via MDPI. {{cite journal}}: Check |pmc= value (help); Check |pmid= value (help)
  14. ↑ Drezga-Kleiminger, Max; Demaree-Cotton, Joanna; Koplin, Julian; Savulescu, Julian; Wilkinson, Dominic (2023-11-27). "Should AI allocate livers for transplant? Public attitudes and ethical considerations". BMC medical ethics. 24 (1): 102. doi:10.1186/s12910-023-00983-0. ISSN 1472-6939. PMC 10683249. PMID 38012660. {{cite journal}}: Check |pmc= value (help); Check |pmid= value (help)
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