The AI Agent in the Room: Informing Objective Decision Making at the Transplant Selection Committee
Bibliographic record
Abstract
Purpose: 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) through autonomous artificial intelligent (AI) agents could provide objectivity in decision-making to solve complex problems.Thus, we examined 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.Methods: This was a hybrid cohort study of adult (≥18-years-old) LT candidates between 2004-2023 from the Scientific Registry of Transplant Recipients (SRTR) database.Patients receiving LT were retrospectively analyzed and a hypothetical cohort of patients with standard absolute contraindications to LT were generated.The AI-SC's performance to 1) waitlist candidates if LT would offer a 6-month or 1-year survival benefit or 2) decline candidates if contraindications to LT were present or if LT would not offer a survival benefit were analyzed.The AI-SC consisted of four LLMs: transplant hepatologist, transplant surgeon, cardiologist, and social worker (Figure ).Cosine similarity index analysis identified the most prevalent variables used by each AI agent of the AI-SC in their decision making.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 (False Negative).Malignancy was the most common cause of death prior to 6-month (21.7%) or 1-year (28.5%) end points (False Positive).Each AI agent used different variables in their decision making and report generation, but indication for LT and recipient age were among the top 5 most important variables used by all members of the AI-SC.Conclusions: 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 study are a provocative step towards making the LT selection process more equitable and objective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".