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Industry-Specific Applications of AI and ML
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Author(s): Saurabh Singhal (GLA University, India), Ajeet Kumar Sharma (Sharda University, India), Akhilesh Kumar Singh (Sharda University, India), Anand Pandey (Sharda University, India)and Avinash Kumar Sharma (Sharda University, India)
Copyright: 2024
Pages: 15
Source title:
Advancing Software Engineering Through AI, Federated Learning, and Large Language Models
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Amarjeet Prajapati (Jaypee Institute of Information Technology, India), Pancham Singh (Ajay Kumar Garg Engineering College, Ghaziabad, India)and Mrignainy Kansal (Netaji Subhas University of Technology (NSUT), Delhi, India)
DOI: 10.4018/979-8-3693-3502-4.ch008
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Abstract
Artificial intelligence in healthcare has the potential to enhance diagnostics, patient care, and medical research. However, trust in AI-driven decision-making processes is crucial as AI systems become more complex. Explainable artificial intelligence (XAI) is a strategy to ensure AI-driven healthcare solutions are efficient and understandable to healthcare professionals and patients. XAI can improve medical practitioners' decision-making processes, increase trust in AI recommendations, and boost patient-doctor communication. Applications include medical imaging, predictive analytics, drug development, and tailored treatment plans. The chapter discusses the ethical and regulatory implications of AI in healthcare, focusing on patient data privacy and security. Future XAI trends will focus on improving patient outcomes and healthcare service quality by making AI systems accessible and consistent with ethical norms.
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