This study presents the xAI-SURG framework, a clinician-centered explainable artificial intelligence (xAI) approach for decision support in the operating room (OR). It addresses the need for explainability in AI systems for enhanced clinician trust and understanding, crucial in high-stakes environments like the OR. The study involved semi-structured interviews with expert perfusionists, focusing on their decision-making during Goal-Directed Perfusion (GDP) in cardiac surgery. A Cognitive Task Analysis (CTA) map was developed, revealing complex decision-making processes. The xAI-SURG framework integrates the Recognition-Primed Decision-Making (RPDM) model with CTA, aligning with perfusionists' cognitive workflows. It aims to enhance patient safety and outcomes by supporting clinicians' naturalistic decision-making with transparent, interpretable AI insights. This paper discusses broader implications for AI in healthcare, emphasizing the balance between AI-driven recommendations and human expertise. Future research directions include longitudinal studies and interface development for broader surgical applications.