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Reducing Kidney Discard With Artificial Intelligence Decision Support: the Need for a Transdisciplinary Systems
Richard Threlkeld1, Lirim Ashiku1, Casey Canfield1
1Engineering Management & Systems Engineering, Missouri University of Science & Technology, 223 Engineering Management 600 W 14th St, MO 65409 Rolla, USA.
Current Transplantation Reports
|January 21, 2022
Summary
A transdisciplinary systems approach can improve artificial intelligence (AI) decision support systems for kidney transplant. This method addresses AI limitations and enhances stakeholder collaboration to reduce kidney discard.
Area of Science:
- Transplant Medicine
- Artificial Intelligence
- Systems Engineering
Background:
- Kidney discard is a significant challenge in organ transplantation, necessitating enhanced coordination among stakeholders.
- Current artificial intelligence (AI) systems face limitations such as overfitting, poor explainability, and inadequate trust, hindering their effective implementation.
- A transdisciplinary approach is crucial for developing robust AI decision support systems in healthcare.
Purpose of the Study:
- To outline a transdisciplinary systems approach for designing artificial intelligence (AI) decision support systems.
- To address the inherent limitations of AI systems in complex healthcare scenarios.
- To improve decision-making processes and reduce kidney discard rates through enhanced AI integration.
Main Methods:
- Employing a transdisciplinary systems approach, integrating expertise from engineering, social science, and transplant healthcare.
- Utilizing systems engineering techniques to visualize system architecture and support multi-perspective solution design.
- Incorporating stakeholder input throughout the AI system design and development lifecycle.
Main Results:
- A transdisciplinary systems approach offers a holistic perspective to overcome AI limitations like overfitting and lack of trust.
- Effective AI decision support requires increased coordination among transplant stakeholders.
- Visualizing system architecture aids in designing AI solutions that address complex problems from multiple viewpoints.
Conclusions:
- Developing AI decision support systems necessitates a cyclical process of system architecture documentation, pain point identification, prototyping, and validation.
- Prioritizing tasks that benefit from AI support by addressing process issues is key to successful implementation.
- A systems-based approach fosters collaboration and iterative refinement for AI in kidney transplantation.
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