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(TSFAM) model, an adaptive human-AI teaming framework designed to enhance hard-to-place kidney acceptance decision-making by integrating transplant surgeons’ individualized expertise with advanced AI analytics (Figure 1). Methods: TSFAM is an innovative solution for complex issues in kidney transplant decision-making support. It employs fuzzy associative memory to capture and codify unique decision-making rules of transplant surgeons. Using the Deceased Donor Organ Assessment (DDOA) and Final Acceptance AI models designed to evaluate hard-to-place kidneys, TSFAM integrates fuzzy logic with deep learning techniques to manage inherent uncertainties in donor organ assessments. Surgeon-specifi c ontologies and membership functions are extracted through interviews. Similar to how a pain scale is used for understanding patients, an ontology ambiguity scale is used to develop surgeon rules (Figure 2). Fuzzy logic captures ambiguity and enables the model to adapt to evolving clinical, environmental, and policy conditions. The structured incorporation of human expertise ensures decision support remains closely aligned with local clinical practices and global best evidence. Results: This novel framework incorporates human expertise into AI decisionmaking tools to support donor organ acceptance in transplantation. Integrating surgeon-defi ned criteria into a robust decision-support tool enhances accuracy and transparency of organ allocation decision-making support. TSFAM bridges the gap between data-driven models and nuanced judgment required in complex clinical scenarios, fostering trust and promoting responsible AI adoption. Conclusions: TSFAM fuses deep learning analytics with subtleties of human expertise for a promising pathway to improve decision-making support in transplant surgery. The framework enhances clinical assessment and sets a precedent for future systems prioritizing human-AI collaboration. Prospective studies will focus on clinical implementation with dynamic interfaces for a more patient-centered, evidencebased model in organ transplantation. The intent is for this approach to be adaptable to individual case scenarios and the diverse needs of key transplant team members