Importance: Transplantation is one of the few areas in medicine where the definitive treatment is rationed. Subjective decision-making pose challenges towards the transplant selection process. It has been proposed that large language models (LLMs) as artificial intelligent (AI) agents could provide objectivity in decision-making to solve complex problems. Objective: To examine the performance of a multidisciplinary selection committee of AI agents (AI-SC) as a proof-of-concept towards objectivity in the liver transplant (LT) selection process. Design: The AI-SC consisted of four LLMs: transplant hepatologist, transplant surgeon, cardiologist, and social worker. Zero-shot prompting with chain-of thought was used. Decisions were made based on clinicodemographic characteristics at time of waitlisting and LT. Setting: National LT cohort. Participants: Adult patients receiving deceased donor LT from 2004-2023 were extracted from the Scientific Registry of Transplant Recipients (SRTR) and clinical vignettes were generated. Standard absolute contraindications to LT were randomly assigned to a subset of patients to expose the AI-SC to cases of patients declined for LT. Exposures: Clinicodemographic characteristics at waitlisting and transplantation. Main Outcomes and Measures: The AI-SCs accuracy with either: 1) listing candidates if LT would offer a 6-month or 1-year survival benefit or 2) declining candidates if contraindications to LT are present or if LT would not offer those survival benefits. Results: Of 8,412 patients, 83.6% were waitlisted and 16.4% had contraindications to LT. The AI-SC was able to accurately identify contraindications to LT (accuracy: 98.2%, 95%CI 97.9%-98.4%), predict 6-month (94.9%, 95%CI 94.4%-95.3%) and 1-year (92.0%, 95%CI 91.4%-92.6%) survival. HCC burden beyond Milan criteria was the most common reason for accepted patients who were declined by AI-SC (False Negative). Malignancy was the most common cause of death prior to 6-month or 1-year end points (False Positive). The AI-SC most frequently did not perceive a lack of social support or severe cardiopulmonary disease as barriers to LT. Conclusions and Relevance: LLMs can be leveraged to simulate the LT-SC meetings and provide accurate, objective insights on patients who may or may not benefit from LT. Lessons learned from this proof-of-concept are a provocative step towards making the LT selection process more equitable and objective.

The authors have declared no competing interest.

This research was supported by funding from the Transplant AI initiative, Ajmera Transplant Centre, University Health Network.

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This study was approved by the Institutional Review Board at University of California, Irvine (#4474).

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The data that support the findings of this study are publicly available through the Scientific Registry of Transplant Recipients. Its study protocol, and statistical analysis plan will be made available following publication upon request. Please contact the corresponding author via email with a study proposal (e.g., statistical plan, study protocol) and details regarding how the data will be used.