Abstract
AI-driven healthcare decision-making is multi-faceted, requiring complex logic to adapt to evolving policies and societal demands. Effective change implementation by healthcare providers and multidisciplinary organ transplant teams depends on adaptive decision-making. The proposed Transplant Surgeon Fuzzy Associative Memory (TSFAM) model introduces a novel approach to Human-AI Teaming, keeping human expertise central while dynamically adjusting to changing requirements. TSFAM employs fuzzy logic to manage imperfect data and human ambiguity, integrating the transplant surgeon perspective with the AI deep learning decision-making tool, creating a resilient solution in this critical domain. By embedding adaptive capabilities into the architecture, TSFAM exemplifies the adaptability of complex systems through transdisciplinary solutions. This paper outlines a method for constructing the TSFAM model where AI is leveraged to extract transplant surgeon-defined rules, using their own ontology and membership functions to guide decision-making when evaluating hard-to-place kidneys. TSFAM enables adaptive decision-making with individualized transplant surgeon rules reflecting their expertise and local environmental needs. TSFAM demonstrates how adaptable models can improve decision-making in dynamic healthcare systems, particularly with kidney transplant acceptance decisions. This approach is anticipated to increase adoption and integration of AI in healthcare, providing a framework for researchers to advance AI models tailored to specific healthcare domain needs.
Recommended Citation
R. Dzieran et al., "Transplant Surgeon Fuzzy Associative Memory (TSFAM): Model for Capturing Surgeon Perspective," Procedia Computer Science, vol. 268, pp. 69 - 76, Elsevier, Jan 2025.
The definitive version is available at https://doi.org/10.1016/j.procs.2025.08.183
Department(s)
Engineering Management and Systems Engineering
Second Department
Electrical and Computer Engineering
Publication Status
Open Access
Keywords and Phrases
deep learning; fuzzy associative memory; healthcare; human-AI systems; organ procurement
International Standard Serial Number (ISSN)
1877-0509
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Elsevier, All rights reserved.
Publication Date
01 Jan 2025
Included in
Electrical and Computer Engineering Commons, Human Factors Psychology Commons, Operations Research, Systems Engineering and Industrial Engineering Commons
Comments
Missouri University of Science and Technology, Grant None