Integration of Genetic Programming in Healthcare Applications: Opportunities, Challenges, and the Imperative for Explainability

Abstract

The rapid advancement of artificial intelligence (AI) has ushered in a new era of innovation in healthcare, promising transformations in diagnosis, treatment, and patient management. Among the diverse subfields of AI, genetic programming (GP) stands out for its capacity to evolve and optimize complex models and solutions with minimal human intervention. By mimicking evolutionary processes, GP offers unique advantages in dealing with the nonlinearity, high dimensionality, and heterogeneity that characterize medical data. However, as AI systems become more deeply embedded in healthcare workflows, concerns regarding explainability, transparency, fairness, and ethical implementation have come to the fore. These concerns are not merely academic; they carry significant implications for clinical adoption, patient trust, regulatory compliance, and the very safety of care delivery. This essay critically examines the integration of genetic programming in healthcare applications, focusing on both the transformative potential and the significant challenges that must be addressed. Drawing on recent literature, including systematic reviews of explainable artificial intelligence (XAI) in healthcare and surveys on creative problem solving within AI, the discussion situates GP within the broader context of AI-driven medical innovation. The essay further interrogates the imperative for explainability and fairness in AI-enabled healthcare, integrating perspectives from recent expert-driven studies and contemporary debates around algorithmic bias, transparency, and the ethics of AI deployment. Ultimately, the essay argues that while genetic programming holds great promise for advancing personalized, efficient, and adaptive healthcare, its responsible integration is contingent upon overcoming critical challenges related to interpretability, fairness, and stakeholder collaboration.

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