A multiagent large language model-based system to simulate the liver transplant selection committee: a retrospective cohort study

Bima J. Hasjim; Ghazal Azarfar; Frank G. Lee; Tayyab S Diwan; Shilpa Raju; Jed Adam Gross; Aman Sidhu; Hirohito Ichii; Rahul G. Krishnan; Muhammad Mamdani; Divya Sharma; Mamatha Bhat

doi:10.1016/j.landig.2025.100966

April 1, 2026The Lancet Digital HealthOpen Access

A multiagent large language model-based system to simulate the liver transplant selection committee: a retrospective cohort study

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Key Points

This research aims to explore the use of a multiagent language model system to simulate the liver transplant selection committee's decision-making process.
Developed a multiagent language model-based system
Conducted a retrospective cohort study
Analyzed the decision-making process of the selection committee
The AI model effectively mimicked the committee's decisions
Demonstrated potential improvements in the transparency of selection processes
Highlighted areas for enhanced decision quality in liver transplant allocations

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Implication

Retrospective cohort study demonstrates a multiagent system's effectiveness in simulating liver transplant decisions, indicating potential for improved selection processes.

Abstract

Transplant AI Initiative, Ajmera Transplant Centre, University Health Network.

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Cite This Study

Hasjim et al. (2026) studied this question.

synapsesocial.com/papers/69d9e47378050d08c1b75054 https://doi.org/10.1016/j.landig.2025.100966

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A multiagent large language model-based system to simulate the liver transplant selection committee: a retrospective cohort study

Key Points

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Abstract

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Cite This Study

A multiagent large language model-based system to simulate the liver transplant selection committee: a retrospective cohort study

Key Points

No abstract yet

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Implication

Abstract

Discussion

Cite This Study

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