Naresh Kumar Satish, Mathieu Mercadier, Cristina Hava Muntean, Anderson Augusto Simiscuka
The cryptocurrency market is widely regarded as one of the most volatile financial markets due to inconsistencies in its pricing factors. Despite this volatility, it continues to attract a large population of investors, many of whom incur significant losses. To address this challenge and support risk assessment for investors, users, and other stakeholders, this paper focuses on forecasting Ethereum prices by analyzing social media sentiment. The study gathers data from sources such as global news headlines and Reddit discussion forums, enhancing it with hybrid sentiment features derived from the VADER, BERT and TextBlob models. These sentiment insights are then correlated with Ethereums financial parameters to establish meaningful relationships within the data, which are used to train machine learning models. The study evaluates the predictive performance of Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory models. Among these, Extreme Gradient Boosting demonstrated superior performance, effectively capturing complex relationships within the data and achieving an R-squared value of 0.982115. To further enhance the studys risk assessment capabilities, the concept of Explainable Artificial Intelligence (XAI) is employed to improve transparency and accountability in the model outcomes. Specifically, Shapley Additive Explanations (SHAP) are used to interpret the feature interactions within the Extreme Gradient Boosting model, thereby increasing its reliability and providing deeper insights into its decision-making process.
Abstraction Liquidity Theory (ALT) develops a formal framework for determining when local problem-solving traces become reusable abstraction assets that reduce downstream search, evaluation, and certification costs. The paper treats abstractions as operational tokens rather than informal artifacts, and evaluates them through declared receivers, opportunity measures, baselines, lifecycle costs, telemetry, evidence validity, transport scope, authority envelopes, hazard constraints, and runtime certificate packets. The manuscript introduces an actor-neutral certification kernel for AI agents and other computational actors. It specifies machine-readable packet schemas, dual exploration and settlement ledgers, finite-sample lower and upper bounds, causal and calibrated-proxy value estimands, mission-validity certificates, adversarial-token rejection, root/finality checks, baseline refresh, deprecation, resurrection, rollback, and kernel-update bridges. The goal is to make abstraction evaluation executable: an agent should be able to parse a packet, verify evidence, admit or reject a token, suspend stale claims, deprecate negative-liquidity tokens, and preserve raw net safe capital under fail-closed rules. The paper further defines Target-valid ALT-CARA, a criterion for certified ASI realization acceleration. Rather than claiming unconstrained ASI achievement, ALT-CARA formalizes time-to-target acceleration relative to a resource-matched baseline upper envelope, under declared capability bases, target-validity certificates, raw net solvency, viability conditions, hazard and authority constraints, transport validity, finality, and causal reproduction evidence. The framework connects AI evaluation, causal inference, runtime verification, risk control, skill reuse, safe exploration, and distributed certification into a single theory of mission-valid safe abstraction capital.
This study investigates the critical challenges associated with ensuring the security and robustness of artificial intelligence (AI) systems, especially within high-stakes applications such as autonomous vehicles, healthcare, and financial technologies. The primary objective is to identify vulnerabilities in AI algorithms and propose effective mitigation strategies. The research emphasizes contemporary threats, including adversarial attacks, algorithmic opacity, data breaches, and the ethical ramifications of AI deployment. A review of current literature reveals that adversarial attacks, where subtle input perturbations cause significant misclassifications, present a considerable risk to AI reliability. Techniques such as robust training, involving training models on adversarial examples, have shown effectiveness in improving resilience, albeit with higher computational demands. The study also explores the importance of explainable AI (XAI) tools like LIME and SHAP, which enhance transparency by clarifying the decision-making processes of complex models. This transparency is vital for fostering user trust, especially in fields like medicine and finance, where understanding AI decisions is essential. XAI approaches enable better oversight and adherence to ethical standards. Data privacy concerns are addressed through methods such as differential privacy, which protects sensitive information by adding noise, and federated learning, which enables decentralized model training without exposing raw data. The findings indicate that these strategies secure data while maintaining model efficacy. By integrating robustness and explainability, this study contributes practical solutions to strengthen AI systems against evolving threats, advancing AI security and fostering trust in these technologies.
Artificial intelligence (AI) has both capabilities and limitations in managing uncertainty, particularly in handling average cases versus extreme outliers. AI excels at analyzing large datasets and predicting typical outcomes, but struggles with rare, critical scenarios that require flexibility beyond its data-driven approach. Integrating human expertise with AI, especially in managing anomalies, enhances AI's potential to address complex situations. The discussion also highlights the tension between rapid innovation and rigid governance, emphasizing the importance of trust and adaptable frameworks for progress in technology and finance. Governance must evolve to allow faster, individualized solutions while maintaining oversight to prevent risks. The concept of "digital twins" improves adaptability and cost efficiency by modeling and simulating physical entities. Transitioning from hardware- to software-driven solutions underscores the need for production agility, enabling industries to adapt without major structural changes. The analysis addresses AI's role in large-scale societal issues like decarbonization, stressing the importance of managing not just mean outcomes but also catastrophic tail risks. AI must identify gaps in its understanding, enhancing efficiency through collaborative training. Technological advancements, including sensors and digital twins, enhance AI's real-time analysis capabilities, expanding applications in robotics, precision agriculture, and decentralized healthcare. The interplay between AI, governance, and innovation shows potential for solving major challenges in finance, healthcare, and sustainability, contingent on integrating adaptive human-AI collaboration.
Paolo Giudici, Alessandro Piergallini, Maria Cristina Recchioni, Emanuela Raffinetti
We consider the problem of developing explainable Artificial Intelligence methods to interpret the results of Artificial Intelligence models for time series data, taking time dependency into account. To this end, we extend the Shapley–Lorenz method, normalised by construction, to Artificial Intelligence for time series, such as neural networks and recurrent neural networks. We illustrate the application of our proposal to a time series of Bitcoin prices, which acts as the response variable, along with time series of classical financial prices, which act as explanatory variables. Three main findings emerge from the analysis. First, recurrent neural networks lead to a better performance, in terms of accuracy and robustness, with respect to classic neural networks. Second, the best performing models indicate that Bitcoin prices are affected mostly by their lagged values, and that their explainability, in terms of classical financial assets, is limited. Third, although limited, the contribution of classical assets to Bitcoin price prediction is well captured by recurrent neural networks.
Systems, technologies, protocols, and infrastructures all face interoperability challenges. It is among the most crucial parameters to give real-world effectiveness. Organizations that achieve interoperability will be able to identify, prevent, and provide appropriate protection on an international scale, which can be relied upon. This paper aims to explain how future technologies such as 6G mobile communication, Internet of Everything (IoE), Artificial Intelligence (AI), and Smart Contract embedded WPA3 protocol-based WiFi-8 can work together to prevent known attack vectors and provide protection against zero-day attacks, thus offering intelligent solutions for smart cities. The phrase zero-day refers to an attack that occurs on the day zero of the vulnerability's disclosure to the public or vendor. Existing systems require an extra layer of security. In the security world, interoperability enables disparate security solutions and systems to collaborate seamlessly. AI improves cybersecurity by enabling improved capabilities for detecting, responding, and preventing zero-day attacks. When interoperability and Explainable Artificial Intelligence (XAI) are integrated into cybersecurity, they form a strong protection against zero-day assaults. Additionally, we evaluate a couple of parameters based on the accuracy and time required for efficiently analyzing attack patterns and anomalies.
Ensuring strict adherence to security during the phases of Android app development is essential, primarily due to the prevalent issue of apps being released without adequate security measures in place. While a few automated tools are employed to reduce potential vulnerabilities during development, their effectiveness in detecting vulnerabilities may fall short. To address this, “Defendroid”, a blockchain-based federated neural network enhanced with Explainable Artificial Intelligence (XAI) is introduced in this work. Trained on the LVDAndro dataset, the vanilla neural network model achieves a 96% accuracy and 0.96 F1-Score in binary classification for vulnerability detection. Additionally, in multi-class classification, the model accurately identifies Common Weakness Enumeration (CWE) categories with a 93% accuracy and 0.91 F1-Score. In a move to foster collaboration and model improvement, the model has been deployed within a blockchain-based federated environment. This environment enables community-driven collaborative training and enhancements in partnership with other clients. The extended model demonstrates improved accuracy of 96% and F1-Score of 0.96 in both binary and multi-class classifications. The use of XAI plays a pivotal role in presenting vulnerability detection results to developers, offering prediction probabilities for each word within the code. This model has been integrated into an Application Programming Interface (API) as the backend and further incorporated into Android Studio as a plugin, facilitating real-time vulnerability detection. Notably, Defendroid exhibits high efficiency, delivering prediction probabilities for a single code line in an average processing time of a mere 300 ms. The weight-sharing transparency in the blockchain-driven federated model enhances trust and traceability, fostering community engagement while preserving source code privacy and contributing to accuracy improvement.
The growing reliance on artificial intelligence in decision-making processes has intensified debates over bias, fairness, and accountability in algorithmic governance. While deep learning models deliver unprecedented predictive performance, their “black box” nature has undermined transparency and public trust, particularly in high-stakes applications such as finance, healthcare, and digital public services. Explainable AI (XAI) has emerged to address this gap by making model reasoning interpretable, yet explainability alone cannot guarantee fairness without verifiable systems of identity and accountability. This study proposes a framework that integrates explainable deep learning with decentralized identity (DID) systems to combat bias, enhance trust, and ensure equitable governance outcomes. In this framework, explainable deep learning models provide human-understandable insights into algorithmic decisions, enabling stakeholders to evaluate reasoning processes. Meanwhile, decentralized identity systems built on blockchain technologies ensure that individuals retain control over their digital identities, reducing risks of centralized manipulation and exclusion. By linking interpretable models with verifiable identity protocols, algorithmic governance can achieve both transparency and fairness while protecting privacy. The integration enables bias detection and correction at both the model and system levels: interpretable models flag discriminatory features, while decentralized identity guarantees equitable access across diverse populations. Applications in digital voting, welfare distribution, and credit scoring illustrate how the framework strengthens accountability and prevents systemic marginalization. Ultimately, combining explainable deep learning with decentralized identity provides a path toward trustworthy and fair algorithmic governance, where decisions are not only accurate but also transparent, inclusive, and ethically aligned with societal values.
Bianca-Mihaela Ganescu, Jonathan Passerat‐Palmbach
Generative AI, exemplified by models like transformers, has opened up new possibilities in various domains but also raised concerns about fairness, transparency and reliability, especially in fields like medicine and law. This paper emphasizes the urgency of ensuring fairness and quality in these domains through generative AI. It explores using cryptographic techniques, particularly Zero-Knowledge Proofs (ZKPs), to address concerns regarding performance fairness and accuracy while protecting model privacy. Applying ZKPs to Machine Learning models, known as ZKML (Zero-Knowledge Machine Learning), enables independent validation of AI-generated content without revealing sensitive model information, promoting transparency and trust. ZKML enhances AI fairness by providing cryptographic audit trails for model predictions and ensuring uniform performance across users. We introduce snarkGPT, a practical ZKML implementation for transformers, to empower users to verify output accuracy and quality while preserving model privacy. We present a series of empirical results studying snarkGPT's scalability and performance to assess the feasibility and challenges of adopting a ZKML-powered approach to capture quality and performance fairness problems in generative AI models.
Shimal Sh. Taher, Siddeeq Y. Ameen, Jihan A. Ahmed
In recent years, cryptocurrencies have experienced rapid growth and adoption, revolutionizing the financial sector. However, the rise of digital currencies has also led to an increase in fraudulent transactions and illegal activities. In this paper, we present a comprehensive study on the detection of fraudulent transactions in the context of cryptocurrency exchanges, with a primary focus on the Ethereum network. By employing various Machine Learning (ML) techniques and ensemble methods, including the hard voting ensemble model, which achieved a remarkable 99% accuracy, we aim to effectively identify suspicious transactions while maintaining high accuracy and precision. Additionally, we delve into the importance of eXplainable Artificial Intelligence (XAI) to enhance transparency, trust, and accountability in AI-based fraud detection systems. Our research contributes to the development of reliable and interpretable models that can significantly improve the cryptocurrency ecosystem security and integrity.
Mohammad H. Hasan, Mohammad Shahriar Rahman, Helge Janicke, Iqbal H. Sarker
As the use of Blockchain for digital payments continues to rise in popularity, it also becomes susceptible to various malicious attacks. Successfully detecting anomalies within Blockchain transactions is essential for bolstering trust in digital payments. However, the task of anomaly detection in Blockchain transaction data is challenging due to the infrequent occurrence of illicit transactions. Although several studies have been conducted in the field, a limitation persists: the lack of explanations for the model's predictions. This study seeks to overcome this limitation by integrating eXplainable Artificial Intelligence (XAI) techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin transactions. The Shapley Additive exPlanation (SHAP) method is employed to measure the contribution of each feature, and it is compatible with ensemble models. Moreover, we present rules for interpreting whether a Bitcoin transaction is anomalous or not. Additionally, we have introduced an under-sampling algorithm named XGBCLUS, designed to balance anomalous and non-anomalous transaction data. This algorithm is compared against other commonly used under-sampling and over-sampling techniques. Finally, the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble classifiers. Our experimental results demonstrate that: (i) XGBCLUS enhances TPR and ROC-AUC scores compared to state-of-the-art under-sampling and over-sampling techniques, and (ii) our proposed ensemble classifiers outperform traditional single tree-based machine learning classifiers in terms of accuracy, TPR, and FPR scores.
Society 5.0 envisions a human-centered society where advanced technologies seamlessly integrate to enhance quality of life, particularly in healthcare. To advance eHealth within this vision, we present a comprehensive framework that integrates the Internet of Medical Things (IoMT), edge computing, and cloud services with Explainable Artificial Intelligence (XAI) and blockchain technology, customized for the 6G era. We introduce the Health Prediction using Cloud Edge 2.0 (HPCE 2.0) algorithm, which employs fuzzy logic to effectively combine historical Electronic Health Records (EHRs) with real-time IoMT data, providing precise and personalized health severity level predictions. To ensure data integrity and security, we integrate the Proof of Authentication 2.0 (PoAh 2.0) consensus mechanism within a blockchain-enhanced IoMT-Edge-Cloud framework. A case study on predicting cardiac arrest in elderly patients demonstrates the practical effectiveness of our framework. Utilizing XAI models such as LIME and SHAP, we provide both local and global explanations for AI predictions, enhancing transparency and trust in healthcare applications; counterfactual explanations offer actionable insights for patients to proactively manage health risks. Security assessments confirm efficient block formation and verification times, validating the system’s scalability and compliance with stringent security standards. This work sets a new standard in digital healthcare by aligning technological advancements with ethical considerations, fostering a human-centered approach consistent with Society 5.0’s vision. Harnessing the capabilities of emerging 6G networks, our framework paves the way for more responsive, secure, and interpretable AI-driven healthcare solutions.
Muhammad Saleem, Muhammad Sajid Farooq, Tariq Shahzad, Arfa Hassan · 8 authors
In the recent era, the practical implementation of Autonomous Vehicular Networks (AVNs) with the vulnerable Vehicle-to-Vehicle (V2V) communication of autonomous vehicles and inadequate intelligent decision-making systems has become a primary concern in smart city mobility. This has led to the traffic congestion concerns such as time wastage, compromised safety, decreased durability and reliability of transportation infrastructure and V2V communication short delay and Roadside Units (RSUs), and reduced traffic flow. To address these issues, secure AVN communication and smart decision-making for autonomous vehicles in smart cities are of utmost importance. It ensures safety on roads, durability of the infrastructure, transparency, reliability, traffic congestion reduction and transportation efficiency. MapReduce is a reliable distributed computing paradigm which is able to analyze and process enormous AVN data in parallel. It contributes to smoother traffic flow by identifying the patterns and providing actionable insights for real-time decision making to decrease congestion. A private blockchain AVN can efficiently solve the problems of data security and reliability by providing tamper-proof record of all the transactions, hence enhancing reliability, and also offering a trusted solution of unauthorized access in real-time V2V communication. Explainable Artificial Intelligence (XAI) which is an efficient way to analyze fairness in traffic data over time providing transparency and availability of intricate traffic patterns, improving real-time traffic management with V2V communication and RSUs and reducing short delays that may occur as well as enabling traffic flow and the development of predictive traffic models that assist in decision making. This research proposed an XAI-based transparent model integrating MapReduce for processing large amounts of data and private blockchain technology for secured and tamper-proof vehicular communication. This proposed model is a promising solution for addressing the AVN data security issues and reliability of the system, mitigating negative effects of traffic congestion, and improving the transparency of decision making on the transport efficiency in smart cities. The proposed model provides a better performance than the previous approaches and gets 96% of the accuracy and 4% of miss rate.
Emerging blockchain payment gateways have facilitated worldwide financial systems with unprecedented efficiency, transparency, and decentralization. Yet, increasingly, such platforms become susceptible to complex financial risks such as fraud at various scales, double-spending, Sybil attacks, and illegal access. The rule-based approaches that were traditionally implemented are no longer adequate to keep up with the evolving threat landscape of decentralized finance (DeFi). This, therefore, serves to strengthen the stance for considering ML models for at least real-time transaction analysis and fraud detection. Even with many models offering good prediction capabilities, the lack of transparency raises serious concerns about issues of interpretability and compliance—especially in environments that are financially regulated. The paper thus delves into the incorporation of explainable machine learning (XML) techniques in blockchain payment risk assessment frameworks. Using model-agnostic tools such as SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) and combining them with very high-end models such as XGBoost and LightGBM, we create interpretable frameworks that enable stakeholders to understand, trust, and verify the risk classifications issued. Our study uses a mixture of real and synthetic blockchain transaction datasets with risk labels and benchmarks each model with respect to accuracy and interpretability. Results show that XML models provide competitive predictive power while also offering actionable explanations useful for detection of anomalies, regulatory audit, and strategic decision-making. We believe that explainable ML is not just achievable but also an absolute prerequisite for sustainable and compliant risk management in blockchain financial infrastructures
Verification of a deep neural network is required as large DNN models are used in machine learning as a service procedure where the server providing a classification service may be insecure and provide invalid classifications. A verification of deep neural networks in a machine learning as a service paradigm requires verification of function evaluation for all functions of a DNN model given a specific input where the service provider and the server do not want to reveal the DNN model to the client. In this paper, we investigate the privacy-preserving verification problem of the DNN model with zero-knowledge proofs. We have developed a KGZ polynomial commitment scheme based on zero-knowledge proof for such DNN verification. We present an efficient DNN verification using KGZ zero-knowledge proof. We have developed a batch-processing algorithm that can significantly reduce the number of function evaluation verifications. We also prove that a malicious server may not manipulate the proposed verification protocol.
Yeajun Kang, Wonwoong Kim, Hyunji Kim, Minwoo Lee · 6 authors
A smart contract is a digital contract on a blockchain. Through smart contracts, transactions between parties are possible without a third party on the blockchain network. However, there are malicious contracts, such as greedy contracts, which can cause enormous damage to users and blockchain networks. Therefore, countermeasures against this problem are required. In this work, we propose a greedy contract detection system based on deep learning. The detection model is trained through the frequency of opcodes in the smart contract. Additionally, we implement Gredeeptector, a lightweight model for deployment on the IoT. We identify important instructions for detection through explainable artificial intelligence (XAI). After that, we train the Greedeeptector through only important instructions. Therefore, Greedeeptector is a computationally and memory-efficient detection model for the IoT. Through our approach, we achieve a high detection accuracy of 92.3%. In addition, the file size of the lightweight model is reduced by 41.5% compared to the base model and there is little loss of accuracy.
Prabhat Kumar, Randhir Kumar, Moayad Aloqaily, A.K.M. Najmul Islam
The next-generation digital revolution is anticipated to be the convergence of Consumer Internet of Things (CIoT) platforms and Metaverse. The use of Metaverse in CIoT can offer a hyper-spatiotemporal, self-sustaining 3D virtual shared space for people to interact, work, and play. Despite the hype around CIoT-inspired Metaverse, security and privacy concerns are seen as the two biggest obstacles in the communication infrastructure and information gathering procedures. The eXplainable Artificial Intelligence (XAI) and blockchain have the potential to reshape and transform the CIoT-inspired Metaverse by bringing significant enhancements in terms of explainability, interpretability, transparency, traceability, and immutability regarding data and communications. In this paper, we first discuss about the security and privacy issues in CIoT-inspired Metaverse. Second, we discuss the importance and properties of XAI and blockchain with a use case to demonstrate the benefits of our proposed architecture to tackle the aforementioned obstacles. Finally, we highlight the future research directions in building futuristic CIoT-inspired Metaverse.
Kar Balan, Shruti Agarwal, Simon Jenni, Andy Parsons · 6 authors
We present EKILA; a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance -- determining the generative model and training data responsible for an AI-generated image. Furthermore, EKILA extends the non-fungible token (NFT) ecosystem to introduce a tokenized representation for rights, enabling a triangular relationship between the asset's Ownership, Rights, and Attribution (ORA). Leveraging the ORA relationship enables creators to express agency over training consent and, through our attribution model, to receive apportioned credit, including royalty payments for the use of their assets in GenAI.
Open access
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Generative Adversarial Networks and Image Synthesis
Indranil Ghosh, Esteban Alfaro, Matías Gámez, Noelia García
Non Fungible Tokens (NFT) and Decentralized Finance (DeFi) assets have seen a growing media coverage and garnered considerable investor traction despite being classified as a niche in the digital financial sector. The lack of substantial research to demystify the dynamics of NFT and DeFi coins motivates the scrupulous analysis of the said sector. This work aims to critically delve into the evolutionary pattern of the NFTs and DeFis for performing predictive analytics of the same during the COVID-19 regime. The multivariate framework comprises the systematic inclusion of explanatory features embodying technical indicators, key macroeconomic indicators, and constructs linked to media hype and sentiment pertinent to the pandemic, nonlinear feature engineering, and ensemble machine learning. Isometric Mapping (ISOMAP) and Uniform Manifold Approximation and Projection (UMAP) techniques are conjugated with Gradient Boosting Regression (GBR) and Random Forest (RF) for enabling the predictive analysis. The predictive performance rationalizes the frameworks' capacity to accurately predict the prices of the majority of the NFT and DeFi coins during the ongoing financial distress period. Additionally, Explainable Artificial Intelligence (XAI) methodologies are used to comprehend the nature of the impact of the explanatory variables. Findings suggest that the daily movement of the NFTs and DeFi highly depends on their past historical movement.
Standard probabilistic models face fundamental challenges such as data scarcity, a large hypothesis space, and poor data transparency. To address these challenges, we propose a novel probabilistic model of data-driven temporal propositional reasoning. Unlike conventional probabilistic models where data is a product of domain knowledge encoded in the probabilistic model, we explore the reverse direction where domain knowledge is a product of data encoded in the probabilistic model. This more data-driven perspective suggests no distinction between maximum likelihood parameter learning and temporal propositional reasoning. We show that our probabilistic model is equivalent to a highest-order, i.e., full-memory, Markov chain, and it can also be viewed as a hidden Markov model requiring no distinction between hidden and observable variables. We discuss that limits provide a natural and mathematically rigorous way to handle data scarcity, including the zero-frequency problem. We also discuss that a probability distribution over data generated by our probabilistic model helps data transparency by revealing influential data used in predictions. The reproducibility of this theoretical work is fully demonstrated by the included proofs.
Digitization and automation have always had an immense impact on healthcare. It embraces every new and advanced technology. Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. This paper proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor's environment, the patient's environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. We also explored the building block technologies of the metaverse. Furthermore, we also investigated the advantages and challenges of a metaverse in healthcare.
The Metaverse is an online universe that combines virtual reality and augmented reality, linked together via a network.It has generated novel experiences that are fully engaging and transpire in real time, facilitating interpersonal communication and dialogue. Virtual environments with 3D space and avatars can boost patient-facing platforms, operational utilisation, digital education, diagnostics, and treatment choices in medicine and ophthalmology. Globally, there is an increasing prevalence of chronic diseases, with an estimated 25 percent of individuals presently contending with multiple chronic health issues. The management of chronic diseases is currently being rethought in light of the development of technology known together as "Smart Healthcare." A prime example is state-of-the-art wearable technology that incentivizes people to embrace healthier lifestyles through the monitoring of physiological indicators and metabolic processes. With better data organisation and analysis, chronic disease patients may benefit from improved health, privacy, and quality of life. Through the examination of physiological data acquired from wearable devices on a patient, Artificial Intelligence (AI) has the capability to generate informed recommendations pertaining to the diagnosis and treatment of illness. These recommendations can be provided by AI. The adoption of blockchain technology (BC) has the potential to significantly advance healthcare in a variety of ways, including decentralised data sharing, user privacy, user empowerment, and dependability in data administration. The potential impact of Wearable Technologies (WT), Artificial Intelligence (AI), and Blockchain Technology (BC) on Chronic Disease Management (CDM) could be a transition in emphasis from the hospital to the patient. This article provides a patient-centered technical framework for controlling chronic diseases using artificial intelligence, blockchain, and wearable technologies. Our proposed architecture depends on Metaverse environment. In order to participate in the Metaverse, both patients and physicians need to sign up on the Blockchain network. After entering, customers will be accompanied by avatars throughout the experience. A comprehensive record of all information gathered during doctor-patient consultations, including text, videos, images, audio, and clinical data, will be compiled, uploaded to the blockchain, and stored in perpetuity. Explainable Artificial Intelligence (XAI) algorithms examine these particulars in order to diagnose and forecast the progression of diseases. We conclude with a discussion of the constraints of this novel paradigm and recommendations for future research.