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Exploring Explainable AI in Healthcare: Challenges and Future Directions
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Author(s): Adla Padma (Vellore Institute of Technology, India), Vasavi Chithanuru (Vellore Institute of Technology, India), Posham Uppamma (Vellore Institute of Technology, India)and R. VishnuKumar (Vellore Institute of Technology, India)
Copyright: 2024
Pages: 35
Source title:
Analyzing Explainable AI in Healthcare and the Pharmaceutical Industry
Source Author(s)/Editor(s): Veena Grover (Noida Institute of Engineering and Technology, India), Balamurugan Balusamy (Manipal Academy of Higher Education, Dubai, UAE), Nallakaruppan M.K. (Vellore Institute of Technology, India), Vijay Anand (Vellore Institute of Technology, India)and Mariofanna Milanova (University of Arkansas at Little Rock, USA)
DOI: 10.4018/979-8-3693-5468-1.ch011
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Abstract
Artificial intelligence (AI) has revolutionized the healthcare industry by making decisions similar to human intelligence. However, the need to illustrate AI predictions based on healthcare data is a challenging task. To address this, Explainable AI (EXAI) has emerged to provide transparent explanations for machine-generated predictions and ensure accuracy in healthcare. This review emphasizes the importance of adopting EXAI in healthcare and discusses its concept of providing reliable AI-based solutions. The authors analyze the most recent developments in EXAI-based technologies and present research findings on their extensive implementation aspects, including the challenges and limitations of existing models. The importance of EXAI in healthcare extends from early disease prediction to intelligent diagnosis. Furthermore, this survey provides insights into the future perspectives of EXAI in healthcare, offering valuable research directions. Integrating EXAI into healthcare can enhance transparency, interoperability, and trust in AI-driven healthcare solutions.
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