Details for: Raj P. Edge AI for Industry 5.0 and Healthcare 5.0 Applications 2025

Raj P. Edge AI for Industry 5.0 and Healthcare 5.0 Applications 2025

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Textbook in PDF format Edge AI is the seamless and spontaneous combination of Edge or Fog computing and AI. It enables acquiring real-time insights, which, in turn, leads to the realization of real-time, people-centric, event-driven, business-critical, process-aware, and knowledge-filled software services and applications. Edge AI for Industry 5.0 and Healthcare 5.0 Applications looks at the unique contributions of Edge AI for developing solutions for Industry 5.0 and Healthcare 5.0. It explains how Industry 5.0 fine tunes the human-machine connection and leverages tiny, high-performance AI-centric processors in IoT edge devices for real-time decision-making and application processing. In recent years, the need for transparency and accountability in AI decision-making has increased as AI has become increasingly common in many industries. This need is met through XAI approaches that provide insight into the inner workings of an AI model, including how it processes incoming data, generates predictions, and makes decisions. Some XAI techniques use visualization tools like decision trees or heatmaps to show how the AI model works for humans. Other methods include AI models that are inherently easier to understand, such as decision rule-based systems or training models for less complex and more readable data representations. In critical sectors such as healthcare, finance, and autonomous cars, where transparency and accountability are essential for the safety and well-being of users, the importance of XAI is particularly evident. Healthcare 5.0 can be developed and implemented using XAI. Integrating cutting-edge technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Big Data analytics, blockchain and 5G, next-generation healthcare systems to enhance efficiency, improve patient outcomes, and revolutionize the healthcare landscape. It is also known as Healthcare 5.0. The patient-centered Healthcare 5.0 paradigm is characterized by a greater focus on preventive healthcare and personalized medicine. Leveraging data from multiple sources, such as wearables, electronic health records, and social media, is key to understanding patient health and delivering proactive, personalized care. XAI can provide the necessary transparency and explainability to help build trust in AI-based healthcare systems. XAI techniques can be used to visualize and explain how an AI model processes patient data and makes decisions, allowing clinicians to better understand and interpret results. This can help clinics diagnose and treat patients more effectively and increase patients’ trust in the healthcare system. XAI can also help identify biases in AI systems that can lead to discriminatory results. By providing a transparent and interpretable view of how an AI model works, XAI enables stakeholders to identify and address distortions in data or algorithms. Explainable AI (XAI) refers to Artificial Intelligence that can provide meaningful and meaningful explanations for their decisions and actions. There are several ways and means of working in XAI. Here are some of the prominent types of XAI: Rule-Based XAI Rule-based interpretation is particularly useful in areas where clarity and interpretation are needed. In the expert system for medical research, the rule-based approach offers several distinct advantages. The main advantage is the traceability of decisions. As the system follows predetermined rules, each decision can be further linked to a specific situational characteristic. This transparency is extremely valuable for physicians who not only need to rely on AI recommendations but also need to understand the theory behind. Model-Based XAI Model-based explanations bridge the gap between the accuracy of complex AI models and the need for transparency and interpretability, especially in critical areas such as medical imaging. It involves neural networks benefits. The accuracy of deep veins in disease classification in clinical imaging has changed the diagnostic approach. But the “black box” nature of these networks in which decisions are made using complex combinations of factors learned from large amounts of data raises concerns about reliability and traceability of results. The approach describing complex AI decisions requires tradeoffs between accuracy intervals and definitions in a simplified model building process. Methods such as decision trees, linear regression, or simple neural network architecture aim to simulate the behavior of complex underlying models. Post Hoc XAI Post hoc annotation represents an important step forward in addressing the annotation challenges posed by complex AI models, especially in areas such as healthcare where the obvious statistics are paramount and the core of the presentations lies in their retrospective nature—reflecting the “black box” decisions of AI models delivering their results. Next, feature-importance methods such as LIME and SHAP are key features of ad hoc translation methods. These approaches seek to shed light on the decision-making process by quantifying the contribution of individual factors or inputs to the sample output. This analysis helps to identify factors that play an important role in AI model decision-making, and elucidate the logic behind forecasting. Focusing on Explainable AI (XAI), the book discusses: The role of XAI in Healthcare 5.0 Best practices, challenges, and opportunities of applying XAI in healthcare setting How to enhance transparency and trust of XAI in Healthcare 5.0 XAI and its methods in predicting healthcare outcomes Other highlights of the book include: 5G communication networks requirements The fusion of IoT, AI, Edge, Cloud, and blockchain Trustworthiness of blockchain technology in healthcare 5.0 and Industry 5.0 The future of trust and the potential of blockchain technology By explaining how Edge AI can transform healthcare and industry, this book empowers researchers and professionals to envisage and implement sophisticated and smart digital solutions

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1. Raj P. Edge AI for Industry 5.0 and Healthcare 5.0 Applications 2025.pdf7.41 MiB