Explainable AI for Healthcare

Abstract

With rapid advancements in artificial intelligence (AI), ranging from traditional machine learning techniques to sophisticated deep learning models, its integration into healthcare systems has accelerated significantly. These developments enable AI to assist in various critical healthcare applications, such as disease diagnosis, treatment planning, predictive analytics, personalised medicine, and medical imaging, which significantly enhance patient care and clinical outcomes. However, the opaque nature of many complex AI models poses a significant challenge, especially given the high-stakes nature of healthcare decisions where patient outcomes can be critically affected. Explainability is essential to mitigate these risks, as it provides transparency that not only aids healthcare professionals in making more informed and confident decisions but also builds trust among patients, allowing them to understand the reasoning behind AI-driven recommendations. This section introduces the foundational concepts of explainable AI (XAI) and highlights prominent methodologies for enhancing model interpretability across various domains. We provide a comprehensive analysis of XAI applications in healthcare, focusing on the unique requirements and challenges posed by different data modalities, including time-series data from monitoring devices, medical text from clinical records, medical images, and audio data such as heart or lung sounds. In conclusion, we discuss the current limitations and challenges of implementing XAI in healthcare settings, while also identifying promising future research directions that could drive further innovation and enhance the reliability of AI-powered healthcare solutions.

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