We will be presenting about this research at the American Transplant Congress (ATC) in Philadelphia, PA from June 1-5. ATC brings together the transplant industry to share the latest science, clinical care, ethics, and more.
Saturday, June 1, 5:30-7pm at the Poster Hall, Exhibit Hall A on the second level:
Richard Threlkeld will be presenting “Investigation of Racial and Ethnic Bias in Kidney Non-Utilization Decisions Using the Deceased Donor Organ Allocation Model”
Rachel Dzieran will be presenting “Fuzzy Associative Memory and Deep Learning Network Model Interface with Transplant Surgeon in Assessing Hard-to-Place Kidneys for Use in Digital Twin Model”.
Tuesday, June 4, 9:15-10am at the Poster Hall, Exhibit Hall A on the second level:
Cihan Dagli will be presenting “AI-Model Interpretation for Deceased Donor Kidney Acceptance Practices”
The Artificial Intelligence and You Symposium hosted by the Center for Science, Technology, and Society was held on April 26th at the Innovation Forum at Missouri S&T. Our team organized a panel discussion in which we discussed measuring and aggregating preferences in AI for kidney transplant healthcare. This included 3 short presentations followed by a panel discussion led by Casey Canfield and joined by Cihan Dagli, Daniel Shank, and Sid Nadendla.
Harishankar Subramanian presenting on explainable AI interfaces.
Harishankar Subramanian spoke on integrating stakeholder input into the design of explainable interfaces. Explainable AI (XAI) helps users to understand system’s processes and logic, appropriately trust the system, effectively manage performance as well as fully understand the system and why the system is operating the way in which it is operating. One of the main findings was that the appropriate interface of an AI will vary based on user expertise and decision-making process. Future work will be done to investigate user understanding of AI and the users ability to judge when to rely on AI recommendations.
Mukund Telukunta presenting on bias in the kidney placement process.
Mukund Telukunta spoke on measuring social fairness preferences from non-expert opinions in kidney transplantation. There is a history of racial bias in the organ transplant process, which needs to be considered in the development of AI tools. Survey participants were presented information, which included the AI’s recommendation as well as the surgeons decision for 10 potential recipients, to evaluate the fairness of the outcomes. The AI decision support system was deemed fair. Future work will consist of gathering expert opinions, such as OPO’s, transplant surgeons, and patients.
Amaneh Babaee presenting on embedding preferences in adaptable AI decision support.
Amaneh Babaee spoke on her research on identifying organizational and individual factors in AI adoption for kidney transplant, specifically in organ procurement organizations (OPO’s). Interviews with OPOs suggest that AI will be useful if it can measurably speed up the allocation process. In addition, the implications of OPO size varies. A large OPO has more higher financial resources to adopt the AI but has a slower decision-making process. A small OPO has less financial resources, but can quickly decide whether or not AI is suitable for them. She is also deploying a survey to measure perceptions of AI. While both studies are still ongoing, it seems that AI has the potential to improve transplant outcomes, but regulatory hurdles may hinder its integration into existing operations.
Panel Discussion led by Casey Canfield (NP) Left to Right: Dr. Shank, Dr. Nadendla, Dr. Dagli, and Student Researchers Harishankar Subramanian, Mukund Telukunta and Amaneh Babaee.
In the panel discussion, one concern was that the AI could make incorrect recommendations, leading to negative outcomes. It is unlikely that this process would be automated and there will always need to be a human-in-the-loop. Transplant centers and OPOs will still need to rely on their expertise to fill in the gaps, since the AI does not have as much information as they do about a particular case.
