The Cultural Calibration Gap in AI-Driven Healthcare: A Hidden Barrier to Global Precision Medicine

Artificial intelligence (AI) is increasingly integrated into healthcare systems, offering advances in diagnosis, treatment, and operational efficiency. However, the cultural assumptions embedded in AI training data often misalign with the sociocultural realities of diverse patient populations—a phenomenon we term the Cultural Calibration Gap (CCG). This conceptual paper defines the CCG as a structural and epistemic deficit, distinct from algorithmic bias or dataset shift, that undermines clinical validity, patient trust, and health equity. We analyze how culturally uncalibrated AI systems exacerbate disparities in diagnosis and care, compromise informed consent, and erode trust, particularly in communities with collective, spiritual, or traditional health frameworks. To address the CCG, we propose a three-pronged strategy: epistemic inclusion of diverse knowledge systems and data sources, participatory design grounded in community-based research, and regulatory reform mandating cultural responsiveness in model validation and oversight. We argue that tackling the CCG is not merely an ethical imperative but a scientific necessity, aligning healthcare AI with the evolving vision of human-centric, adaptive Healthcare 5.0. Culturally calibrated AI systems promise not only improved precision and trustworthiness but also more equitable and contextually meaningful care across global health landscapes.