Explainable AI & Model Interpretability in Healthcare: Challenges & Future Directions

Abstract

Explainable Artificial Intelligence (XAI) in healthcare seeks to make the behavior and reasoning of complex machine learning models transparent to stakeholders. As AI systems become increasingly prevalent in clinical decision support, their “black-box” nature raises concerns about trust, safety, and ethical use. This paper presents a comprehensive overview of explainability and model interpretability in healthcare, emphasizing theoretical foundations, key challenges, and emerging solutions. We begin by defining XAI and its importance in the medical context, outlining how interpretability can enhance clinician and patient trust without sacrificing model performance. We then review the broad applications of XAI across healthcare domains, illustrating its growing adoption. Next, we delve into key challenges that impede the integration of XAI into clinical workflows: the need for trust and transparency, the complexity of state-of-the-art models, ethical and regulatory requirements for explainability, data privacy constraints, and practical barriers to deployment in healthcare settings. This future entails interdisciplinary collaboration, standardized evaluation metrics for explanations, and regulatory frameworks that encourage safe, transparent AI in medicine. By addressing current challenges and leveraging emerging methods, XAI can foster appropriate trust in AI-driven healthcare and ultimately improve decision-making and patient outcomes.