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Purpose: The equitable distribution of donor kidneys is crucial to maximizing transplant success rates and addressing disparities in healthcare data. This study examines potential gender bias in the Deceased Donor Organ Allocation Model (DDOA) by using machine learning and AI to analyze its impact on kidney discard decisions to ensure fairness in accordance with medical ethics. Methods: The study employs the Deceased Donor Organ Allocation Model (DDOA) model (https://ddoa.mst.hekademeia.org/#/kidney) to predict the discard probability of deceased donor kidneys using donor characteristic from the OPTN Deceased Donor Dataset (2016-2023). Using the SRTR SAF dictionary, the dataset consists of 18,029 donor records, where gender was assessed for its effect on discard probability. ANOVA and t-test determines whether there is a statistically significant difference between the discard percentages for female and male donors by changing the donor gender data alone. If the p-value obtained from the t-test is less than the significance level (typically 0.05), we reject the null hypothesis and conclude that there is a significant difference. Otherwise, we fail to reject the null hypothesis. Results: Figure 1 visualizes the differences in discard percentages between female and male donor kidneys, with an unbiased allocation system expected to show no difference (i.e., a value of zero). To assess the presence of gender bias, statistical analyses, including t-tests and ANOVA were performed. The t-test comparing female and male kidney discard rates yielded a t-statistic of 29.690228, with a p-value of 3.586956e-189 < 0.05 significance threshold. This result leads to the rejection of the null hypothesis, indicating a significant difference was found between the mean when altering only the donor gender attribute in the DDOA model making it play a significant role in discard decisions. Conclusions: The study highlights that a significant difference was found between the mean by altering only the donor gender attribute, contributing to kidney discard rates in the DDOA model. These findings reinforce the need for greater transparency in organ allocation models and a reconsideration of the demographic criteria used in the evaluation process. Future research should refine algorithms to minimize biases in organ allocation and investigate kidney discard disparities in transplantation. 

Purpose: AI models for kidney transplant acceptance must be rigorously evaluated for bias to ensure equitable healthcare access. This study investigates demographic and clinical biases in the Final Acceptance Model (FAM), a donor-recipient matching deep learning model that complements surgeons’ decision-making process in predicting whether to accept available kidneys for their patients with end of stage renal disorder. Methods: AI models for kidney transplant acceptance must be rigorously evaluated for bias to ensure equitable healthcare access. This study investigates demographic and clinical biases in the Final Acceptance Model (FAM), a donor-recipient matching deep learning model that complements surgeons’ decision-making process in predicting whether to accept available kidneys for their patients with end of stage renal disorder. Results: There is no significant racial bias in the model’s predictions (p=1.0), indicating consistent outcome across all racial combinations between donors and recipients. Gender-related effects as shown in Figure 1, while statistically significant (p=0.008), showed minimal practical impact with mean differences below 1% in prediction probabilities. Significant difference Clinical factors involving diabetes and hypertension showed significant difference (p=4.21e-19). The combined presence of diabetes and hypertension in donors showed the largest effect on predictions (mean difference up to -0.0173, p<0.05), followed by diabetes-only conditions in donors (mean difference up to -0.0166, p<0.05). These variations in clinical factor predictions showed bias against groups with comorbidities. Conclusions: The biases observed in the model highlight the need to improve the algorithm to ensure absolute fairness in prediction.