Abstract
Purpose of Review: A transdisciplinary systems approach to the design of an artificial intelligence (AI) decision support system can more effectively address the limitations of AI systems. By incorporating stakeholder input early in the process, the final product is more likely to improve decision-making and effectively reduce kidney discard.
Recent Findings: Kidney discard is a complex problem that will require increased coordination between transplant stakeholders. An AI decision support system has significant potential, but there are challenges associated with overfitting, poor explainability, and inadequate trust. A transdisciplinary approach provides a holistic perspective that incorporates expertise from engineering, social science, and transplant healthcare. A systems approach leverages techniques for visualizing the system architecture to support solution design from multiple perspectives.
Summary: Developing a systems-based approach to AI decision support involves engaging in a cycle of documenting the system architecture, identifying pain points, developing prototypes, and validating the system. Early efforts have focused on describing process issues to prioritize tasks that would benefit from AI support.
Recommended Citation
R. Threlkeld and L. Ashiku and C. I. Canfield and D. B. Shank and M. A. Schnitzler and K. L. Lentine and D. A. Axelrod and A. C. Battineni and H. Randall and C. H. Dagli, "Reducing Kidney Discard with Artificial Intelligence Decision Support: The Need for a Transdisciplinary Systems Approach," Current Transplantation Reports, Springer, Nov 2021.
The definitive version is available at https://doi.org/10.1007/s40472-021-00351-0
Department(s)
Engineering Management and Systems Engineering
Second Department
Psychological Science
Keywords and Phrases
Artificial Intelligence; Decision-Making; Kidney Discard; Systems Science; Transdisciplinary
International Standard Serial Number (ISSN)
2196-3029
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2021 Springer, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
15 Nov 2021
Comments
Casey Canfield, Daniel Shank, Mark Schnitzler, Krista Lentine, Henry Randall, and Cihan Dagli are supported by National Science Foundation Award #2026324.