The proposed Transplant Surgeon Fuzzy Associative Memory (TSFAM) conceptual model offers an innovative approach to integrating augmented intelligence in healthcare systems, particularly in organ transplantation. Focusing on Human–AI Teaming, TSFAM emphasizes the central role of human expertise, while AI dynamically adapts to evolving clinical and environmental conditions. The model addresses uncertainty and ambiguity in decision-making by employing fuzzy logic, particularly for evaluating hard-to-place kidneys, where traditional data-driven models often prove inadequate. TSFAM combines transplant surgeon expertise with deep learning, resulting in a resilient and adaptable system that reflects surgeon-defined rules. These rules are extracted using AI model interaction based on the surgeon’s unique ontology and membership functions, which helps ensure that decisions align with individual preferences and the local healthcare environment. Designed with systems engineering principles, this conceptual model empowers healthcare teams to respond effectively to evolving conditions, policies, and societal needs. The paper details the construction and implementation of TSFAM, showcasing how it improves decision-making in organ transplant scenarios. It highlights the potential for improved integration of AI into healthcare by tailoring systems to domain-specific challenges, providing a conceptual foundation for future researchers to advance AI models that meet the multifaceted needs of healthcare professionals and complex systems.