In recent years, the use of deep learning models in sensitive applications increased exponentially. There is a strong need of having a mechanisms for a transparent and secure inference verification. To this end, we propose a system leveraging Zero-Knowledge Proofs (ZKPs) and Blockchain technologies to ensure the validity of model inferences without revealing neither input data nor model details.In this paper, we propose a system that is capable of making non-interactive proofs that are verified on a Blockchain thus creating a trustless environment between the prover and the verifier. The solution is based on the Easy Zero-Knowledge Inference (EZKL) [1] library and leverages ZK-SNARK [2] proofs. We provide a detailed descriptions of the system’s architecture, the implementation as well as the benefits of this approach in enhancing transparency and security in Artificial Intelligence (AI) applications.
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.
The procedure for obtaining an equivalency certificate for international educational recognition is typically complicated and opaque, and differs depending on the nation and system. To overcome these issues and empower students, this study suggests a revolutionary assessment tool that makes use of blockchain technology, chatbots, the European Credit Transfer and Accumulation System (ECTS), and Explainable Artificial Intelligence (XAI). Educational equivalency assessments frequently face difficulties and lack of openness in a variety of settings. The suggested solution uses blockchain for tamper-proof record keeping and secure data storage, based on the capabilities of each component. This improves the blockchain’s ability to securely store application data and evaluation results, fostering immutability and trust. Using the distributed ledger feature of blockchain promotes fairness in evaluations by preventing tampering and guaranteeing data integrity. The blockchain ensures data security and privacy by encrypting and storing data. Discuss how XAI might explain AI-driven equivalence choices, promoting fairness and trust, by reviewing pertinent material in each domain. Chatbots can improve accessibility by streamlining data collection and assisting students along the way. Transparency and efficiency are provided via ECTS computations that integrate XAI and chatbots. Emphasizing the availability of multilingual support for international students, we also address issues such as data privacy and system adaption. The study recommends further research to assess the multifaceted method in practical contexts and improve the technology for moral and efficient application. In the end, both students and institutions will benefit from this, as it can empower individuals and promote international mobility of degree equivalization.
Federated learning (FL) enables collaborative training of machine learning models across distributed medical data sources without compromising privacy. However, applying FL to medical image analysis presents challenges like high communication overhead and data heterogeneity. This paper proposes novel FL techniques using explainable artificial intelligence (XAI) for efficient, accurate, and trustworthy analysis. A heterogeneity-aware causal learning approach selectively sparsifies model weights based on their causal contributions, significantly reducing communication requirements while retaining performance and improving interpretability. Furthermore, blockchain provides decentralized quality assessment of client datasets. The assessment scores adjust aggregation weights so higher-quality data has more influence during training, improving model generalization. Comprehensive experiments show our XAI-integrated FL framework enhances efficiency, accuracy and interpretability. The causal learning method decreases communication overhead while maintaining segmentation accuracy. The blockchain-based data valuation mitigates issues from low-quality local datasets. Our framework provides essential model explanations and trust mechanisms, making FL viable for clinical adoption in medical image analysis.
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
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.
Blockchain technology has been integrated into a wide range of applications in various sectors, such as finance, supply chain, health, and governance. However, the participation of a few actors with malicious intentions challenges law enforcement authorities, regulators and other users. These challenges revolve around dealing with an array of illegal activities such as asset trades in dark markets, receiving payments for cyber-attacks, and facilitating money laundering. Developing an efficient mechanism to identify malicious actors in blockchain networks is a pressing need to build confidence among the stakeholders and ensure regulatory adherence. The raw data of blockchain transactions do not readily reveal the dynamic behavioural changes and their interconnection between transactions and accounts. These behavioural patterns can be useful for identifying malicious actors. Machine Learning (ML)-based models for early warning and/or detection are considered one of the potential approaches. In ML, feature engineering plays a crucial role in enhancing the predictive performance of a model. This study proposes different categories of features and unified feature extraction approaches for raw Bitcoin and Ethereum transaction data and their interconnection information. As far as we are aware, there has been no study that considered a feature engineering approach for identifying malicious activities. The significance of the engineered features was validated against eight classifiers, including Random Forest (RF), XG-boost (XG), Silas, and neural network-based classifiers. The results showed that these features contribute to higher classification accuracy and higher Area Under the Receiver Operating Characteristic Curve (AUC) value for both Bitcoin and Ethereum transactions. This work also analysed the influence of engineered features in classification using the eXplainable Artificial Intelligence (XAI) technique SHapley Additive exPlanations (SHAP) values. The feature importance scores confirmed the significance of the proposed engineered features towards implementing classification models to identify, target and disrupt malicious activities in blockchain networks.
Andrea Pelosi, Claudio Felicioli, Andrea Canciani, Fabio Severino
While Artificial Intelligence (AI) is making significant strides in a variety of sectors, an exclusive focus on accuracy can overlook the critical aspect of trustworthiness, especially in contexts where it should be a primary concern. In this paper, we propose a novel framework for the development of trustworthy AI systems, leveraging Hybrid Distributed Ledger Technology (Hybrid DLT). We explore the concept of shifting from an accuracy-based paradigm to an approach where trustworthiness is an integral part of the design. Our framework facilitates collaboration between different entities across the data preparation, model training, and the classification phase of a supervised learning ML solution. It uses a shared ledger which offers a tamper-resistant audit log of every operation, ensuring non-repudiation and replicability. We discuss how employing our proposed framework leads to significantly enhanced trustworthiness in AI systems.
The consumer Internet of Things (IoT) applications in particular smart cities are mostly equipped with Internet-connected networked devices to improve city operations by giving access to a massive amount of valuable information. However, these smart devices in a smart city environment mostly use public channels to access and share data among different participants. This has introduced a great interest in using authentication and key agreement (AKA) mechanisms and intrusion detection systems (IDS) based on artificial intelligence (AI) techniques. However, most of the AKA mechanisms have high computation and communication costs and cannot be trusted completely. On the other hand, the AI-based IDS are treated as blackbox by the security analyst due to their inability to explain the reasons behind the decision. In this direction, we have integrated blockchain-based AKA mechanism with explainable artificial intelligence (XAI) for securing smart city-based consumer applications. Specifically, first, the participating entities communicate with each other in a secure manner to exchange data using a blockchain-based AKA mechanism. On the other hand, we have used SHapley Additive exPlanations (SHAP) mechanism to explain and interpret the prominent features that constituent most in the decision. The practical implementation of the proposed framework proves the efficiency over other recent state-of-the-art techniques.
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
3 source records
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.