Prototypes
Deceased Donor Kidney Assessment
Webinars
2023, Aug 1, 6-7pm CT. AI in Kidney Acceptance Research Update Webinar, Online. [Slides]
Journal Publications
Subramanian, H. V. , Canfield, C., & Shank, D. B. (2024). Designing Explainable AI to Improve Human-AI Team Performance: A Medical Stakeholder-Driven Scoping Review. Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2024.102780
Subramanian, H. V., Canfield, C., Shank, D. B., & Kinnison, M. (2023). Combining Uncertainty Information with AI Recommendations Supports Calibration with Domain Knowledge. Journal of Risk Research, 26(10), 1137-1152. https://doi.org/10.1080/13669877.2023.2259406
Ashiku, L. & Dagli, C. (2023). Identify Hard-to-Place Kidneys for Early Engagement in Accelerated Placement With a Deep Learning Optimization Approach. Transplantation Proceedings, 55(1), 38-48. https://doi.org/10.1016/j.transproceed.2022.12.005
Elder, H., Canfield, C., Shank, D. B., Rieger, T., & Hines, C. (2022). Knowing When to Pass: The Effect of AI Reliability in Risky Decision Contexts. Human Factors. https://doi.org/10.1177/00187208221100691
Threlkeld, R., Ashiku, L., Canfield, C., Shank, D., Schnitzler, M., Lentine, K., Axelrod, D., Battineni, A. C. R., Randall, H., Dagli, C. (2021). Reducing Kidney Discard with Artificial Intelligence Decision Support: The Need for a Transdisciplinary Systems Approach. Current Transplantation Reports, 8, 263-271. https://doi.org/10.1007/s40472-021-00351-0
Conference Proceedings
Threlkeld, R., Ashiku, L., Dagli, C., Dzieran, R., Canfield, C., Lentine, K., Schnitzler, M., Marklin, G., Rothweiler, R., Speir, L., & Randall, H. (2023). AI-Enabled Digital Support to Increase Placement of Hard-to-Place Deceased Donor Kidneys. American Journal of Transplantation. (extended abstract published from American Transplant Congress)
Ashiku, L., Threlkeld, R., Dagli, C., Schnitzler, M., Canfield, C., Lentine, K., & Randall, H. (2022). Donor Disposition AI Model to Predict Transplant for Recovered Deceased Donor Kidneys. American Journal of Transplantation, 22(suppl 3), 652-653. (extended abstract published from American Transplant Congress)
Ashiku, L., Threlkeld, R., Canfield, C., & Dagli, C. (2022). Identifying AI Opportunities in Donor Kidney Acceptance: Incremental Hierarchical Systems Engineering Approach. IEEE Systems Conference (SysCon), pp. 1-8, doi: 10.1109/SysCon53536.2022.9773875.
Schnitzler, M. A., Dagli, C., Canfield, C., Dzebisashvili, N., Varma, C., Axelrod, D., Lentine, K., Ouseph, R., & Randall, H. (2020). Using Artificial Intelligence Tools for Identification of High Risk Transplant Recipients for Focused Management. American Journal of Transplantation, 20(suppl 3), 283–284. (extended abstract published from American Transplant Congress)
Subramanian, H. V., Canfield, C., Shank, D. B., Andrews, L., & Dagli, C. (2020) Communicating Uncertain Information from Deep Learning Models in Human Machine Teams. Proceedings of the American Society for Engineering Management.
Presentations
2023. Subramanian, H. V., Canfield, C., & Shank, D. B. Integrating Stakeholder Input into the Design of Explainable AI Interfaces: An Application in Kidney Transplant Healthcare. Institute of Industrial and Systems Engineers, New Orleans, LA.
2022. Subramanian, H. V., Canfield, C., Shank, D. B., & Dagli, C. The Role of Explainable AI in a Decision Support System for Kidney Transplant Placement. American Society for Engineering Management, Tampa, FL.
2021. Subramanian, H. V., Canfield, C., & Shank, D. B. Role of Uncertainty Information and Domain Knowledge in Use of Artificial Intelligence Recommendations. Society for Risk Analysis, Online.
2021. Subramanian, H. V., Canfield, C., Elder, H., Ashiku, L., Threlkeld, R., Hines, C., Dagli, C., Shank, D., Lentine, K., Schnitzler, M., & Randall, H. Engaging Stakeholders in the Transplant Community to Design Artificial Intelligence Decision Support. American Society for Engineering Management, Online.
2020. Subramanian. H. V., Canfield, C., Shank, D., Andrews, L., & Dagli, C. Designing Communications for AI Recommendations with Uncertain Truth. Society for Risk Analysis (SRA) Annual Meeting, Online.
2020. Elder, H., Canfield, C., Shank, D. B., & Hines, C. Can AI recommendations encourage riskier decision-making with higher reliability? Society for Risk Analysis (SRA) Annual Meeting, Online.
2019. Canfield, C., Shank, D. B., Andrews, L., & Dagli, C. Communicating Uncertainty in Deep Learning Models for High Stakes Decisions [Poster Platform]. Society for Risk Analysis (SRA) Annual Meeting, Arlington, VA.