Evaluating the Influence of AI Support on Surgeon Decision-Making in Kidney Transplant
Location
Havener Center, Miner Lounge / Wiese Atrium, 9:30am-11:30am
Start Date
4-2-2026 9:30 AM
End Date
4-2-2026 11:30 AM
Presentation Date
April 2, 2026; 9:30am-11:30am
Description
Improving kidney utilization requires timely and informed decisions across transplant stakeholders. Over the past four years, this project has developed and evaluated AI systems to improve decision quality, efficiency, and trust in transplant decision-making. In the final year, we are conducting an experiment using SimUNet, a web-based simulator that replicates the United Network for Organ Sharing (UNOS) DonorNet interface, to examine how AI influences surgeons’ offer decisions and how the timing of AI presentation affects its use.
Transplant surgeons will review 18 de-identified donor cases under three randomized within-subject conditions: (1) no AI, (2) pre-decision AI, and (3) post-decision AI. The AI provides a predicted probability of kidney acceptance for a given candidate. We will evaluate decision performance (offer decisions) and efficiency (decision time) across conditions. This study will help determine how and when AI best supports transplant decision-making to improve kidney utilization.
Biography
Eyuel Getahun is a doctoral student in Engineering Management at Missouri University of Science and Technology, where he earned his M.S. in Engineering Management in 2024. His research examines how artificial intelligence (AI) technologies influence kidney offer decisions and aims to generate evidence to support decision-making in the transplant space.
Meeting Name
2026 - Miners Solving for Tomorrow Research Conference
Department(s)
Engineering Management and Systems Engineering
Document Type
Poster
Document Version
Final Version
File Type
event
Language(s)
English
Rights
© 2026 The Authors, All rights reserved
Evaluating the Influence of AI Support on Surgeon Decision-Making in Kidney Transplant
Havener Center, Miner Lounge / Wiese Atrium, 9:30am-11:30am
Improving kidney utilization requires timely and informed decisions across transplant stakeholders. Over the past four years, this project has developed and evaluated AI systems to improve decision quality, efficiency, and trust in transplant decision-making. In the final year, we are conducting an experiment using SimUNet, a web-based simulator that replicates the United Network for Organ Sharing (UNOS) DonorNet interface, to examine how AI influences surgeons’ offer decisions and how the timing of AI presentation affects its use.
Transplant surgeons will review 18 de-identified donor cases under three randomized within-subject conditions: (1) no AI, (2) pre-decision AI, and (3) post-decision AI. The AI provides a predicted probability of kidney acceptance for a given candidate. We will evaluate decision performance (offer decisions) and efficiency (decision time) across conditions. This study will help determine how and when AI best supports transplant decision-making to improve kidney utilization.
Comments
Advisor: Casey I. Canfield, canfieldci@mst.edu