Explainable AI in Healthcare

TLDR

Artificial Intelligence integrated with smart wearable devices can predict health conditions, yet its black‑box nature undermines accountability and trust, prompting the need for Explainable AI (XAI) techniques. This paper proposes using XAI to analyze and diagnose health data, aiming to enhance accountability, transparency, result tracing, and model improvement in healthcare. The authors present an XAI approach that applies explainability methods to AI‑based health data analysis and diagnosis, facilitating transparent decision‑making.

Abstract

Artificial Intelligence (AI) is an enabling technology that when integrated into healthcare applications and smart wearable devices such as Fitbits etc. can predict the occurrence of health conditions in users by capturing and analysing their health data. The integration of AI and smart wearable devices has a range of potential applications in the area of smart healthcare but there is a challenge in the black box operation of decisions made by AI models which have resulted in a lack of accountability and trust in the decisions made. Explainable AI (XAI) is a domain in which techniques are developed to explain predictions made by AI systems. In this paper, XAI is discussed as a technique that can used in the analysis and diagnosis of health data by AI-based systems and a proposed approach presented with the aim of achieving accountability. transparency, result tracing, and model improvement in the domain of healthcare.