The opportunities posed by the integration of continuous learning AI in medical care are ground-breaking and can open possibilities in diagnostics, treatment delivery optimization, and health equity. But such systems hold the danger of widening or exacerbating pre-existing disparities when they continue to be neutral in fairness and unadaptive to changing patient populations, data environments, and clinical settings. To overcome this challenge, the framework that allows adjustments in alignment between algorithmic decision making and concepts of health equity is needed. This work presents the rationale behind adaptive fairness of continuous learning AI healthcare systems and the shortcomings of traditional fairness paradigms and the risks of equity changes over repeated training, or drift. It underlines the necessity of conceptual underpinnings of fairness, namely, distributive, procedural, and relational justice, and facilitates evolution models that install fairness auditing, lifecycle surveillance, and supervision processes. Emphasis is on ethics, including aligning value, transparency, and resilience of any automation bias in clinical decision-making. The suggested framework combines federated learning and explainable AI to minimize disparities, as well as justice-oriented multilevel models where fairness is framed within technical, institutional and societal contexts. This study aligns the concept of fairness with the adaptive and dynamic paradigm, offering a guide on how to make AI in healthcare trustworthy and its continued success by revising its moral targets.