Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Solid Organ Transplant

Abstract

Advances in systems immunology, such as new biomarkers, offer the potential for highly personalized immunosuppression regimens that could improve patient outcomes. In the future, integrating all of this information with other patient history data will likely have to rely on artificial intelligence (AI). AI agents can help augment transplant decision making by discovering patterns and making predictions for specific patients that are not covered in the literature or in ways that are impossible for humans to anticipate by integrating vast amounts of data (e.g. trending across numerous biomarkers). Similar to other clinical decision support systems, AI may help overcome human biases or judgment errors. However, AI is not widely utilized in transplant to date. In this rapid review, we survey the methods employed in recent research in transplant-related AI applications and identify concerns related to implementing these tools. We identify three key challenges (bias/accuracy, clinical decision process/AI explainability, AI acceptability criteria) holding back AI in transplant. We also identify steps that can be taken in the near term to help advance meaningful use of AI in transplant (forming a Transplant AI Team at each center, establishing clinical and ethical acceptability criteria, and incorporating AI into the Shared Decision Making Model).

Smart match: revolutionizing organ allocation through artificial intelligence

“…The lack of explainability in AI decision-making processes adds complexity, requiring efforts to enhance understanding. Establishing clinical and ethical acceptability criteria, forming a Transplant AI Team, and integrating AI into the Shared Decision-Making Model can address these challenges (Clement and Maldonado, 2021 ). Ensuring equity and avoiding biases in organ allocation is vital for fostering fairness and patient-centered care.…”

Multiview Clustering to Identify Novel Kidney Donor Phenotypes for Assessing Graft Survival in Older Transplant Recipients

“…However, while the identification of differences in outcomes for donor clusters may suggest that future efforts to create prognostic models inclusive of cluster assignment are valuable, this should not be performed without external validation and should not lead to inequitable distribution of organs for older adults. 46,47 Recent changes to kidney allocation in the United States have focused on incorporating utility goals such as increasing post-transplant survival rates. Inherent to this goal of utility is to adequately capture donor kidney longevity, ascertained by the KDPI (derived from the KDRI) and inclusive of an opt-in system for older candidates to receive lower-quality donor kidneys.…”

Deceased Donor Characteristics and Kidney Transplant Outcomes

“…These include donor risk scores in the setting of DCD kidneys ( 85 ), donor-recipient characteristics ( 86 ), donor-specific features ( 87 ), monitoring of perfusion parameters and assessment of tissue viability function ex situ ( 88 ), molecular diagnostics ( 89 ), and machine learning and artificial intelligence (AI) algorithms ( 90 - 92 ). The latter remains in its infancy, with tremendous potential to augment the decision-making regarding transplantation ( 93 ), but requires more granular data, generalizability, and validation across different population cohorts to enter mainstream use. Such AI tools must provide survival probabilities for kidney transplant candidates to proceed with an individual organ offer versus remaining on the waiting-list to allow a meaningful decision to be made about transplantation.…”