Explainable AI and computational intelligence in healthcare: Application to clinical decision support and personalized medicine
Clinical decision-making; Computational intelligence; Electronic health records; Explainable artificial intelligence (XAI); Health prediction systems; interpretability; Medical data analysis
Human intelligence system simulation has made significant strides in several areas, including clinical decision-making using medical imaging and electronic health records, health referral systems, discovering recommended medications and vaccines, recognizing prescribed errors, and real-time data analysis. Therefore it is essential to discover patterns and transfer knowledge in the medical domain. The obstacles at the level of data collection, data analysis, model development, decision-making, and ethical concerns need to be addressed. It is vital that clinical interpretation tools associated with both hardware and software employed by medical professionals be precisely examined when rendering decisions regarding diagnoses and therapies related to the diagnosis. Computer scientists generally lack training in medical concepts specific to their field. Another crucial aspect is that black box algorithms based on artificial and computational intelligence are opaque and devoid of logical justification. Owing to these limitations, the technique of eXplainable Artificial Intelligence (XAI) models is explored in this chapter, primarily focusing on improving the interpretability of computational models. Specific objectives of this chapter are to: a) discuss the role that CI techniques and methods in the construction of an intelligent health prediction system; b) demonstrate the multiple CI paradigms utilized in medical prediction; and c) present recent case studies to showcase the performance of the computational intelligent models. 2026 Elsevier Inc. All rights reserved.
Kannan, M.; Sindhu, V.; Umamageshwari, D.; Keerthika, K.
Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics;pp.245-271
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https://doi.org/10.1016/B978-0-44-333234-0.00020-0 https://www.scopus.com/pages/publications/105033423078?origin=resultslist
Kannan M., Department of Computer Science, CHRIST University, Bangalore, India; Sindhu V., Department of Computer Science, CHRIST University, Bangalore, India; Umamageshwari D., Department of Computer Science, CHRIST University, Bangalore, India; Keerthika K., Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India