Explainable Artificial Intelligence in the Healthcare

Abstract

There has been a tremendous amount of progress in artificial intelligence (AI) in the healthcare industry in recent years. In healthcare, AI applications are challenging because of explainability issues. Researchers have attempted to overcome the limitations of AI methods because they are black boxes. XAI is a tool that can perform decisions and predictions within a model that can also provide explanations, which are more powerful than AI techniques such as deep learning. AI techniques such as machine learning, which rely on internal representations of models at the base of their models, have made great advances in recent years in AI. There are many types of machine learning algorithms, such as support vector machines (SVMs), random forests, probabilistic graphical models, reinforcement learning (RL), and deep learning (DL) neural networks. It is difficult to understand these models, despite their high performance. The key to understanding, trusting, and effectively managing these new, artificially intelligent partners, however, lies in explanations for many critical applications in defence, medicine, finance, and law. AI systems that provide explanations for internal decisions, behaviours, and actions are emerging to assist with communication to health care professionals. In order to gain the trust of clinicians, XAI explains the outcomes in ways that can be easily understood by them. It is through this chapter that the promise of XAI in healthcare is elaborated, and it is expressed in a positive light when designing XAI applications in healthcare.

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