Analysis of Explainable AI Methods in Healthcare

Abstract

Artificial intelligence (AI) using deep learning models has been extensively used across several fields, including medical imaging and healthcare applications. In the medical domain, every judgment or choice is laden with danger. A physician will meticulously assess a patient's condition prior to developing a coherent explanation based on the patient's symptoms and/or an examination. Consequently, for AI to be a viable and acceptable instrument, it must replicate human judgment and interpretative abilities. Explainable AI (XAI) seeks to elucidate the underlying knowledge of deep learning's black-box models, clarifying the decision-making processes involved. This study presents an overview of the latest XAI approaches used in healthcare and associated medical imaging applications. We outline and classify the forms of XAI, emphasizing the techniques used to enhance interpretability in medical imaging subjects. Furthermore, we concentrate on the complex XAI issues within medical applications and provide guidance for enhancing the interpretability of deep learning models using XAI principles in medical picture and text analysis. This survey offers guidance for developers and researchers in future studies on clinical subjects, especially with medical imaging applications. A revolutionary transition towards Healthcare 5.0 is anticipated in the healthcare sector. It broadens the operational scope of Healthcare 4.0 and utilizes patient-focused digital wellbeing. Healthcare 5.0 emphasizes real-time patient monitoring, environmental management and wellbeing, as well as privacy adherence using assistive technologies such as artificial intelligence (AI), Internet of Things (IoT), big data, and supportive networking channels. Nonetheless, healthcare operational processes, the verifiability of predictive models, resilience, and the absence of ethical and legal frameworks pose possible obstacles to the actualization of Healthcare 5.0.