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Explainable AI (XAI) for Green AI-Powered Disease Prognosis
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Author(s): Shashank Mittal (O.P. JIndal Global University, India), Priyank Kumar Singh (Doon University, Dehradun, India), Saikat Gochhait (Symbiosis Institute of Digital and Telecom Management, Symbiosis International (Deemed), Pune, India & Samara State Medical University, Russia)and Shubham Kumar (University of Minnesota, Minneapolis, USA)
Copyright: 2024
Pages: 20
Source title:
Green AI-Powered Intelligent Systems for Disease Prognosis
Source Author(s)/Editor(s): Ashish Khanna (Maharaja Agrasen Institute of Technology, India)and Saikat Gochhait (Symbiosis Institute of Digital and Telecom Management, Symbiosis International (Deemed), India & Samara State Medical University, Russia)
DOI: 10.4018/979-8-3693-1243-8.ch008
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Abstract
Accurate disease prognosis is crucial for improved healthcare outcomes. Artificial intelligence (AI) offers immense potential in this domain, but traditional “black-box” models lack interpretability. This chapter explores the integration of Explainable AI (XAI) with Green AI, a resource-efficient and sustainable approach to AI development. They discuss how XAI can enhance trust in Green AI models for disease prognosis, mitigate potential biases, and promote responsible AI development. They highlight the challenges of balancing interpretability with efficiency and propose future research directions to unlock the full potential of XAI for Green AI-powered disease prognosis. This approach has the potential to revolutionize healthcare by providing accurate, transparent, and environmentally friendly tools for early disease detection and improved patient outcomes.
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