Abstract
The integration of artificial intelligence (AI) into healthcare decision-making holds promise for improving equity and transparency, particularly in organ transplantation. This paper examines the practical and ethical implications of applying AI to the kidney transplant donor supply chain, with specific attention to limitations in existing data collection practices. Although algorithmic matching systems are used to rank donor–recipient pairs, current datasets often lack systematic documentation of physicians’ rationales for deviating from standard allocation rankings. These undocumented “rank-jumping” decisions introduce opacity into the process and may unintentionally perpetuate inequities in access to transplantation.
The central argument of this paper is that greater data transparency and granularity regarding physician decision-making can improve both the fairness and effectiveness of AI-supported allocation systems. By incorporating interpretable representations of clinical judgment and contextual factors into algorithmic models, AI systems can better support consistent, accountable decision-making while preserving necessary human oversight. Where access to comprehensive historical data is limited, simulation and synthetic data approaches are proposed to explore how data completeness and model design influence allocation outcomes.
The paper also considers the broader ethical implications of AI-enabled organ allocation, including issues of privacy, autonomy, and distributive justice. By focusing on the kidney transplant supply chain, this research contributes actionable insights into how AI can be responsibly deployed to reduce health disparities while maintaining ethical integrity in life-critical healthcare processes.