Abstract
The emergence of advanced AI technologies such as expert systems, fuzzy logic, neural networks, and genetic algorithms can directly address people's health needs by providing personalized advice to users while at the same time collecting valuable data related to aspects of diseases and treatments. These technologies have been in use for quite some time and have evolved to the point where they have matured into practical decision support systems that can be used to assist people in health management and promotion efforts. This chapter presents a survey of the current progress and ongoing research in the area of health-related AI technology and end-user products. With the ever-growing trajectory of technology and the increasing demand to reduce healthcare costs while providing better quality healthcare, employing AI as part of or adjunct to medical processes will become increasingly important from both economic and social perspectives. The classical problems of the healthcare industry include providing people with new and better treatments, more effective ways of preventing, detecting, and combating multifaceted chronic diseases, and ways of caring for the ever-increasing numbers of senior citizens.
Keywords:
Advanced AI technologies, Expert systems, Fuzzy logic, Neural networks, Genetic algorithms, Personalized health advice, Disease treatment data, Decision support systems, Health management, Health promotion, AI in healthcare, End-user health products, Healthcare technology, Reducing healthcare costs, Quality healthcare, Medical processes, Chronic disease prevention, Senior citizen care, Multifaceted disease detection, Economic and social healthcare perspectives
References
Avacharmal, R. (2021). Leveraging Supervised Machine Learning Algorithms for Enhanced Anomaly Detection in Anti-Money Laundering (AML) Transaction Monitoring Systems: A Comparative Analysis of Performance and Explainability. African Journal of Artificial Intelligence and Sustainable Development, 1(2), 68-85.
Vaka, D. K. “Artificial intelligence enabled Demand Sensing: Enhancing Supply Chain Responsiveness.
Mahida, A. A Review on Continuous Integration and Continuous Deployment (CI/CD) for Machine Learning.
Mandala, V., & Surabhi, S. N. R. D. (2021). Leveraging AI and ML for Enhanced Efficiency and Innovation in Manufacturing: A Comparative Analysis.
Chintale, P. (2020). Designing a secure self-onboarding system for internet customers using Google cloud SaaS framework. IJAR, 6(5), 482-487.
Bansal, A. (2021). OPTIMIZING WITHDRAWAL RISK ASSESSMENT FOR GUARANTEED MINIMUM WITHDRAWAL BENEFITS IN INSURANCE USING ARTIFICIAL INTELLIGENCE TECHNIQUES. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND MANAGEMENT INFORMATION SYSTEMS (IJITMIS), 12(1), 97-107.
Korada, L. (2021). Unlocking Urban Futures: The Role Of Big Data Analytics And AI In Urban Planning–A Systematic Literature Review And Bibliometric Insight. Migration Letters, 18(6), 775-795.
Vaka, D. K. " Integrated Excellence: PM-EWM Integration Solution for S/4HANA 2020/2021.
Mahida, A. A Comprehensive Review on Generative Models for Anomaly Detection in Financial Data.
Mandala, V., & Surabhi, S. N. R. D. (2020). Integration of AI-Driven Predictive Analytics into Connected Car Platforms. IARJSET, 7 (12).
Chintale, P. SCALABLE AND COST-EFFECTIVE SELF-ONBOARDING SOLUTIONS FOR HOME INTERNET USERS UTILIZING GOOGLE CLOUD'S SAAS FRAMEWORK.
Bansal, A. (2021). INTRODUCTION AND APPLICATION OF CHANGE POINT ANALYSIS IN ANALYTICS SPACE. INTERNATIONAL JOURNAL OF DATA SCIENCE RESEARCH AND DEVELOPMENT (IJDSRD), 1(2), 9-16.
Vaka, D. K. (2020). Navigating Uncertainty: The Power of ‘Just in Time SAP for Supply Chain Dynamics. Journal of Technological Innovations, 1(2).
Mahida, A. Cross-Border Financial Crime Detection-A Review Paper.
Bansal, A. (2020). An effective system for Sentiment Analysis and classification of Twitter Data based on Artificial Intelligence (AI) Techniques. International Journal of Computer Science and Information Technology Research, 1(1), 32-47.
Dilip Kumar Vaka. (2019). Cloud-Driven Excellence: A Comprehensive Evaluation of SAP S/4HANA ERP. Journal of Scientific and Engineering Research. https://doi.org/10.5281/ZENODO.11219959
Downloads
Download data is not yet available.