ABSTRACT
Explainable artificial intelligence (XAI) has emerged as a critical field in the biomedical domain, offering insights into the decision-making process of complex AI models. The chapter discusses the importance of interpretability in biomedical applications, emphasizing the need for transparency and trust in AI-driven healthcare decisions. It also covers the challenges posed by the black-box nature of deep learning models and the potential risks associated with unexplainable AI in critical medical scenarios. Next, the chapter delves into the concept of XAI and its techniques, such as rule-based explanations, feature importance analysis, and visual explanations. An XAI framework for interpreting high-resolution computed tomography (HRCT)chest analysis for detecting pulmonary arterial hypertension in COVID-19 patients using convolutional neural networks is discussed to showcase how XAI enhances clinicians’ understanding, aids validation, and promotes human-AI collaboration. Furthermore, the chapter addresses the challenge of explaining models and the potential biases introduced during the explanation process. The ethical considerations surrounding XAI are also explored. Additionally, it emphasizes the importance of interdisciplinary collaboration between AI researchers, clinicians, and regulatory authorities to harness the full potential of XAI in healthcare.
Overall, the book chapter provides a comprehensive overview of the role of XAI in the biomedical world. It emphasizes the importance of explainability in AI-driven healthcare systems and guides researchers and policymakers seeking to navigate the landscape of XAI in biomedical applications.
