Metacognitive Calibration and Explainable AI in Healthcare Decision Support: Bridging Interpretability, Trust, and Clinical Application

Abstract

The rapid integration of artificial intelligence (AI) and machine learning (ML) into healthcare and biomedical engineering has yielded transformative advances in diagnostic accuracy, treatment optimization, and clinical workflow efficiency. However, the deployment of complex, black-box models in high-stakes medical decision-making has surfaced a critical challenge: the opacity of model reasoning, which undermines clinician trust, impedes regulatory approval, and limits real-world adoption. This paper presents a comprehensive review and synthesis of explainable AI (XAI) methodologies applied to healthcare decision support systems (CDSS), with a particular focus on how metacognitive calibration—the capacity of AI systems to accurately assess and communicate their own uncertainty—can bridge the interpretability gap. Drawing upon ten foundational references spanning orthopedic biomechanics, large language models (LLMs), vegetation and climate modeling, and AI auditing, this study develops a unified framework for trustworthy AI in clinical and scientific applications. Key contributions include a taxonomy of XAI methods, an analysis of metacognitive calibration benchmarks, an examination of the fundamental limits of AI auditing, and recommendations for integrating transformer-based architectures with XAI tools in orthopedic biomechanics and clinical prediction. The findings indicate that hybrid frameworks combining deep learning with post-hoc explanation generation, coupled with metacognitive monitoring, offer the most promising path toward transparent, trustworthy, and clinically useful AI systems.