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Challenges and Limitations of Explainable AI in Healthcare
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Author(s): Veena Grover (Noida Institute of Engineering and Technology, Greater Noida, India)and Mahima Dogra (Noida Institute of Engineering and Technology, Greater Noida, India)
Copyright: 2024
Pages: 14
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.ch005
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Abstract
Explainable AI (XAI) is at the forefront of healthcare innovation. It has the potential to revolutionize clinical decision-making, improve patient care, and transform healthcare delivery. Despite having said that, the integration of XAI into healthcare system is not devoid of challenges and limitations. This chapter explores the multifaceted landscape of shortcomings faced in the process of implementation of XAI in healthcare, providing valuable insights into the complexities and hurdles that needs to be given direction in order to utilize its full potential in interpreting AI in enhancing healthcare results. One of the initial challenges encountered in the implementation of XAI is the inherent complexity of healthcare data. This chapter is an attempt to identify and address challenges and embrace a collaborative commitment to transparency, fairness, and accountability, and also to navigate the complex nature of the Explainable AI in the process of implementation to lead to a new age of interpretable and trustworthy AI-generated healthcare systems.
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