At the National Innovation Forum hosted by The Alliance, Dr. Casey Canfield from Missouri S&T presented our team’s work on designing appropriate AI for organ allocation and acceptance—AI that not only predicts outcomes but also aligns with the real-world needs of transplant stakeholders.
She summarized the three deep learning models that help predict organ utilization, provisional acceptance, and final acceptance. These tools aim to support both Organ Procurement Organizations (OPOs) and transplant centers, particularly in time-sensitive or uncertain decision scenarios.
The core focus of the presentation was that AI must be fair and explainable. The team is actively investigating bias in training data to incorporate stakeholder input to define fairness more inclusively. She also highlighted the importance of explainable AI (XAI) to help users understand and trust the system.
This work is building toward a 2026 field trial using SimUNet, a behavioral science platform that mimics real-world transplant decision-making environments.
To see Dr. Canfields full presentation slides, please click below.
You can watch Dr. Canfield’s presentation—along with the full day of sessions—here!