Interpretable Models for Healthcare: Enhancing Clinical Decision Support Systems

Abstract

Interpretable machine learning (IML) models have gained increasing attention as the demand for transparency and accountability in AI systems grows. This paper explores various interpretable machine learning models, discussing their methodologies, advantages, limitations, and applications. It also examines techniques for enhancing the interpretability of complex models and the role of IML in critical sectors such as healthcare, finance, and legal systems. The aim is to provide a comprehensive overview of the current state of interpretable machine learning and its future prospects.