Implementing Explainable AI in Healthcare: Techniques for Interpretable Machine Learning Models in Clinical Decision-Making

The integration of explainable artificial intelligence (XAI) in healthcare is revolutionizing clinical decision-making by providing clarity around complex machine learning (ML) models. As AI becomes increasingly critical in medical fields—ranging from diagnostics to treatment personalization—the interpretability of these models is crucial for fostering trust, transparency, and accountability among healthcare providers and patients. Traditional "black-box" models, such as deep neural networks, often achieve high accuracy but lack transparency, creating challenges in highly regulated, high-stakes settings like healthcare. Explainable AI addresses this issue by employing methods that make model decisions understandable and justifiable, ensuring that clinicians can interpret, trust, and apply AI recommendations safely and effectively.

This paper presents a comprehensive analysis of explainable AI techniques specifically tailored for healthcare applications, focusing on two primary approaches: intrinsic interpretability and post-hoc interpretability. Intrinsic techniques, which design models to be naturally interpretable (e.g., decision trees, logistic regression), enable clinicians to directly trace and understand the rationale behind model predictions. Post-hoc techniques, on the other hand, provide interpretability for complex models after they have been trained. Examples include SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and saliency maps in medical imaging, each of which provides insights into how and why specific predictions are made.

This study also examines the unique challenges of implementing explainable AI in healthcare, such as balancing accuracy with interpretability, addressing the diversity of stakeholder needs, and ensuring data privacy. Through real-world case studies—such as early sepsis detection in intensive care units and the use of saliency maps in radiology—the paper demonstrates how explainable AI improves clinical workflows, enhances patient outcomes, and fosters regulatory compliance by enabling transparency in automated decision-making. Ultimately, this work underscores the transformative potential of explainable AI to make machine learning models not only powerful but also trustworthy, actionable, and ethical in the context of healthcare.