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Explainable Artificial Intelligence for Trustworthy Decision-Making in Smart Wearable Textiles
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Author(s): Pawan Kumar (Ajay Kumar Garg Engineering College, Ghaziabad, India), Mandeep Singh (Bennett University, Greater Noida, India), Juli Yadav (Ajay Kumar Garg Engineering College, Ghaziabad, India), Shalbha Chaudhary (R.D. Engineering College, Ghaziabad, India)and Pranshu Saxena (Bennett University, Greater Noida, India)
Copyright: 2026
Pages: 20
Source title:
Next-Generation Electronic Textiles and Conductive Materials for Smart Wearables
Source Author(s)/Editor(s): Pranshu Saxena (Bennett University, India), Mandeep Singh (Bennett University, India), Sanjay Kumar Singh (University School of Automation and Robotics, Guru Gobind Singh Indraprastha University, East Delhi, India)and Mamoon Rashid (Bahrain Polytechnic, Bahrain)
DOI: 10.4018/979-8-3373-4287-0.ch009
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Abstract
Due to the black-box nature of AImodels, they face significant critical challenges, particularly in safety domains such as healthcare, banking, and autonomous vehicles. Explainable Artificial Intelligence (XAI) technology aims to address these challenges by providing explanations that enable informed predictive decisions, while ensuring a transparent, fair and accountable system. This research paper presents a comprehensive study on how XAI enhances the quality of smart wearable textiles for patient care, providing greater efficiency and enabling trustworthy decisions that ultimately benefit patient health. Due to significant advancements in artificial intelligence (AI), the use of AI-based smart wearable devices, particularly in the healthcare sector, has increased substantially. These real-time, AI-based wearable devices continuously monitor medical health of patients information, enabling physicians to detect diseases early and track patients during their recovery.
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