Philosophical Foundations for Explainable AI in Healthcare Explored

The increasing use of medical AI faces a significant challenge: the opacity of many machine learning models. While explainable AI (XAI) aims to clarify these predictions, particularly in high-stakes medical contexts, the definition of an "adequate" explanation remains debated. This research bridges a gap by integrating insights from the philosophy of science and medicine into contemporary XAI discussions.The paper argues that existing philosophical frameworks offer necessary conditions for a robust approach to explainability in healthcare. It highlights three key areas: the role of causality in medical reasoning, the epistemic and relational aspects of trust, and the criteria for explanatory adequacy tailored to diverse stakeholder needs.By synthesizing philosophical analysis with current medical AI developments, the study proposes principles for XAI system design. These principles aim to ensure explanations are not only epistemically sound but also align with the practical and knowledge-based requirements of clinical decision-making, thereby enriching the conceptual foundations of medical XAI.