Diagnostic and Surgical Uses of Explainable AI (XAI)

Abstract

In current years, the field of artificial intelligence (AI) has exhibited a significant impact potential in the view of medicine. Nevertheless, the implementation of AI in clinical applications is blocked by challenges associated with explainability. Particularly in techniques like deep learning, the inherent black-box nature of AI methods possesses difficulties in comprehending decision-making processes. Researchers have delved into the reality of explainable AI (XAI) to overcome this limitation, which offers dual insights into the model's rationale and decision-making capabilities. In this chapter, the current scenario in the combined effect of XAI in medical diagnosis and surgical applications is discussed. The focus of this discussion is to provide a valuable reference for medical professionals and AI scientists involved in the design and implementation of medical XAI applications. In domains like healthcare, the explainability and interpretability of machine learning and AI systems are foremost concerns. Reliance in the outcomes of these systems are significant, especially in medical field where errors can lead to severe consequences. Identifying the need for transparency, XAI has bloomed as a pivotal area of research in this medical domain. Its main objective is to unravel the intricacies of complex machine learning models, addressing the challenges possessed by their translucent nature. XAI algorithms such as Local Interpretable Model-Agnostic Explanations and SHapley Additive exPlanations machine learning models have gained prominence to build trust. These algorithms provide explanations for the black-box models that made prediction, offering perceptions into feature importance and building trust in the system's reliability. This study serves as a comprehensive versatility between machine learning, XAI, and medical applications; the main objective to contribute important insights to both medical professionals and AI researchers toward surgery and diagnosis is extensively reported. The scope of the AI in the surgery and diagnosis is also critically reviewed.

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