The AI Agent in the Room: Informing Objective Decision Making at the Transplant Selection Committee

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. 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. 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. National LT cohort. 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. Clinicodemographic characteristics at waitlisting and transplantation. The AI-SC’s 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. 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. 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. Can a multidisciplinary selection committee of artificial intelligence-based agents (AI-SC) accurately select liver transplant (LT) candidates based on potential survival benefit and contraindications to LT? Clinical vignettes were generated from 8,412 LT candidates from the Scientific Registry of Transplant Recipients (SRTR). Of these, 16.4% were randomly assigned standard absolute contraindications to LT. The AI-SC (GPT-4, OpenAI) reviewed and selected LT candidates with accuracies of 98.2% in identifying contraindications to LT, 94.9% in predicting 6-month survival benefit, and 92.0% in predicting 1-year survival benefit. Multi-agent models may be leveraged to provide guidance towards objective decision-making in transplant candidacy.