The integration of artificial intelligence (AI) in medical diagnostics has the potential to revolutionize health care by improving accuracy, efficiency, and decision-making. However, the adoption of AI-powered diagnostic systems is challenged by their inherent black-box nature, making it difficult to understand how they generate predictions. This chapter explores the role of explainable AI (XAI) in enhancing trust and transparency in AI-driven medical diagnosis. It examines key challenges, including the lack of interpretability in complex models and the risks of bias, which can undermine clinical reliability and patient confidence. To address these concerns, the chapter discusses various XAI techniques, including model-agnostic approaches like local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP), as well as model-specific methods for deep learning systems. These techniques provide insights into AI-generated diagnoses, fostering greater clinician trust and improving communication between healthcare providers and patients. Additionally, the chapter highlights ethical and regulatory considerations necessary for the responsible deployment of AI in medical settings. To support practical understanding, the chapter includes a detailed pseudocode illustrating the implementation of XAI methods in a clinical diagnostic context, offering a step-by-step view of how interpretability can be operationalized. By promoting transparency and accountability, XAI not only enhances the safety and effectiveness of AI-assisted medical care but also ensures compliance with ethical standards and legal frameworks. As AI continues to evolve, integrating explainability into diagnostic systems will be essential for ensuring their widespread acceptance and responsible use in healthcare.

Integrating Generative Pre-Trained Transformer (GPT-4) in healthcare heralds a transformative era, automating routine tasks and empowering physicians to focus on complex patient care. GPT-4's role in patient education simplifies medical terminology, fosters a knowledgeable patient base, and promotes adherence to care plans. This study presents a secure framework for integrating GPT-4, Natural Language Processing (NLP), and patient data in healthcare using Secure Multi-Party Computation (MPC). The framework ensures secure data exchange among stakeholders in healthcare data analysis. Emphasizing security, privacy, and regulatory compliance, the study underscores collaboration with privacy, security, and healthcare experts. The amalgamation of GPT-4 and NLP advances research and care and upholds data protection standards. The revolutionary use of MPC marks a paradigm shift, promising enhanced patient outcomes and securing the future of healthcare.

The metaverse, a virtual multiuser environment, has garnered global attention for its potential to offer deeply immersive and participatory experiences. As this technology matures, it is evolving in tandem with emerging innovations such as Web 3.0, Blockchain, nonfungible tokens, and cryptocurrencies like Bitcoin, which play pivotal roles in the metaverse economy. Robust Bitcoin networks must be modelled for the metaverse environment in Industry 5.0 platforms to ensure the metaverse’s sustained growth and relevance. Industry 5.0 is poised to experience significant economic expansion, driven in large part by the transformative influence of metaverse technology. Researchers have actively explored diverse strategies and approaches to address the unique challenges and opportunities presented by current Bitcoin networks, highlighting the limitless potential for enhancing anonymity and privacy while navigating this exciting digital frontier. By addressing the diverse anonymity and privacy evaluation attributes, the lack of clarity regarding the prioritisation of these attributes and the variability in data, this modelling approach can be categorised as a form of multiple attribute decision-making (MADM). This review seeks to achieve three main objectives: firstly, to identify research gaps, obstacles, and problems within scholarly literature, which is crucial for assessing and modelling Bitcoin networks to succour the metaverse environment of Industry 5.0; secondly, to pinpoint theoretical gaps, proposed solutions, and benchmarking of Bitcoin networks; and thirdly, to offer an overview of the existing validation and evaluation methods employed in the literature. This review introduced a unique taxonomy by intersecting “Bitcoin networks based on blockchain aspects” with “anonymity and privacy development attributes aspect.” It emphasised the study’s significance and innovation. The results illustrate that employing MADM techniques is highly suitable for modelling Bitcoin networks to support the metaverse within the context of Industry 5.0. This thorough review is an invaluable resource for academics and decision-makers, offering perspectives regarding the improvements, applications, and potential directions for evaluating Bitcoin networks to bolster the metaverse environment of Industry 5.0.