Swarm Learning (SL) offers a transformative solution to the challenges posed by growing data security regulations and privacy concerns. It creates new opportunities for research in fields such as healthcare, finance, and smart technologies. This decentralized machine learning framework harnesses the collective intelligence of distributed nodes, each holding private data, and uses blockchain technology to ensure data privacy. The framework constructs a shared model by aggregating insights from each node without compromising the security of local data. Motivated by the goals of enhancing model performance and deepening the understanding of model aggregation, this study systematically tested various merging strategies on three datasets—MNIST, BloodMNIST, and Blood Cell Cancer (ALL)—within a simulated Swarm Learning environment. As a result, we developed the Adaptive Performance-Based Merge Strategy (AP-BMS), a novel method that dynamically selects the optimal merging algorithm within the Swarm network based on continuous model evaluations. This strategy improved performance by approximately 1% on the MNIST dataset, 6% on BloodMNIST and 4% on the Blood Cell Cancer (ALL) dataset. The AP-BMS marks a significant advancement in local model aggregation and further accelerates the evolution of Swarm Learning and its application in secure, decentralized machine learning environments.
BACKGROUND The rapid integration of deep learning into nuclear medicine promises to revolutionize precision oncology but faces a critical "trust gap." As AI models become "black boxes," clinicians struggle to verify the integrity of individual diagnostic inferences, leaving systems vulnerable to adversarial attacks and silent model drift. OBJECTIVE This formative evaluation proposes and validates an in-silico proof-of-concept for a blockchain-agnostic Proof of Inference (PoI) protocol. The objective is to establish a standard of Computational Integrity for AI-assisted workflows in nuclear medicine without exposing proprietary model weights or patient privacy. METHODS he PoI protocol leverages Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs), specifically the Groth16 proof system. An in-silico feasibility study was conducted using a 1.2-million-parameter U-Net model on synthetic 128×128 Ga-68 PSMA-11 PET slices. Proof generation and verification latencies were benchmarked using an NVIDIA A100 GPU and a standard CPU, respectively. RESULTS The architectural analysis demonstrates that the protocol successfully offloads computational burden to the prover (cloud server). In our empirical benchmarking, cryptographic proof generation required 28.81 seconds per inference. Crucially, client-side verification of the proof was completed in 448.59 milliseconds, demonstrating that cryptographic attestation can be integrated into existing PACS viewers with sub-second, clinically acceptable latency. CONCLUSIONS The proposed PoI protocol provides a feasible forensic support layer for medical AI. By shifting clinical trust from institutional reputation to deterministic cryptographic assurance, this infrastructure creates a tamper-evident audit trail essential for algorithmic accountability in decentralized healthcare environments.
Open access
Artificial Intelligence in Healthcare and Education
Semantic communication is a paradigm shift in wireless systems that transmits semantic information, such as intent, context, and meaning, instead of raw data to reduce redundant data. At its core, semantic knowledge bases (SKBs) store and organize the contextual knowledge required for accurate encoding, decoding, and reasoning over semantic information. Recently, large language models (LLMs), pretrained on massive and diverse text corpora, have been integrated into SKBs to generate high-quality semantic embeddings, enable zero-shot retrieval of relevant knowledge, and support complex inference tasks across a wide range of domains. However, since the training corpus of LLM may include outdated, malicious, or privacy-sensitive content, LLM-enabled SKBs should be updated efficiently and verifiably to remove specific data without retraining from scratch. In this article, we first conduct a survey on related works and then propose a model-agnostic proof of unlearning framework for LLM-driven SKBs in semantic communications. Specifically, we track the evolution of the unlearning process by measuring drifts in the LoRA adapter subspace. We then execute successive reverse steps and generate the proof trace that a verifier can compare to provide a quantitative and verifiable unlearning guarantee. Finally, experimental results demonstrate the effectiveness of our proposed framework.
Blockchain and Federated Learning (FL) provide a strong framework for distributed, privacypreserving machine learning in the medical field. In order to provide safe and effective model training, this framework assists in handling sensitive patient data from lung disease diagnosis, such as CT scans, X-rays, and clinical records. The proposed approach improves distributed machine learning security, privacy, and integrity, particularly in delicate fields like healthcare. Contributions from other datasets help the model get better, but patient data is kept private and blockchain guarantees the integrity of the updates to the model. ZeroKnowledge Proofs (ZKP) guarantee that customers can demonstrate the accuracy of their model upgrades without disclosing any personal information. FLBC- ZKP uses cryptographic proofs to remove this requirement for confidence. FLBC-ZKP models exhibit competitive accuracy rates in healthcare applications, guaranteeing confidentiality and privacy without compromising predictive performance. Contri- butions from other datasets improve the model, but patient information is kept confidential and the blockchain ensures the accuracy of model updates. Compared to regular FL, FLBC-ZKP delivers superior privacy and security through blockchain and ZKP, making it particularly suitable for sensitive healthcare data, while maintaining high accuracy. The accuracy data throughout federated learning rounds for a different approach, FLBC-ZKP slightly surpasses the other methods as the number of rounds increases.
Abdullah Ayub Khan, Abdul Khalique Shaikh, Roobaea Alroobaea, Abdullah M. Baqasah · 7 authors
Advanced precision oncology has the potential to revolutionize the current infrastructure of precision oncology, especially in cancer diagnosis and ongoing monitoring, owing to the strong development and advancement of the Internet of Medical Things (IoMT) and Blockchain Distributed Ledger Technology (BDLT). In order to improve cancer diagnosis accuracy and real-time patient monitoring, this paper introduces a novel Blockchain-enabled secure IoMT architecture that incorporates state-of-the-art multi-modal data fusion algorithms, such as weighted fusion. A comprehensive picture of patient health is made possible by this proposed architecture, which presents a novel mechanism for the safe, dynamic aggregation of various datasets, including as genetic portfolios, medical imaging, and wearable sensory-enabled data, as we assess the existing solutions. However, a BDLT-enabled immutable distributed ledger that uses cutting-edge encryption techniques to protect patient privacy while guaranteeing data immutability, decentralized access controls, fine-grained data availability, and traceability are among the main goals. This proposed architecture's unique context-aware data fusion algorithm greatly outperforms traditional techniques, achieving a diagnostic accuracy of 97.10%, precision of 98.25%, F1-scroe of 0.97, and sensitivity of 96.85%. Furthermore, the incorporation of BDLT enhanced security and privacy protection by eliminating single points of failure and attaining 100% immutability. When handling, organizing, and processing dynamic data, a latency of less than 250 ms is calculated. Through simulations using real-world case studies, the proposed work is tested, enhancing the system's reliability while also showcasing its scalability, energy efficiency, and robustness. Based on the examination of the simulation findings, we are able to reach parameters such as data integrity, throughput exceeding 300 transactions per second, and resource utilization efficiency optimized up to 85% in comparison to other state-of-the-art methodologies. It guarantees dependable functioning even with fluctuating computational loads. The findings demonstrate its ability to provide precise, secure, and useful insights instantly, revolutionizing real-time monitoring and cancer diagnosis.
, a novel, multi-layered framework designed to overcome these critical limitations in the Medical IoT domain. Med-Q Ledger integrates a permissioned Hyperledger Fabric for transactional integrity with a scalable Holochain Distributed Hash Table for high-volume telemetry, achieving horizontal scalability and sub-second commit times. To fortify long-term data security, the framework incorporates post-quantum cryptography (PQC), specifically CRYSTALS-Di lithium signatures and Kyber Key Encapsulation Mechanisms. Real-time, privacy-preserving intelligence is delivered through an edge-based federated learning (FL) model, utilizing lightweight autoencoders for anomaly detection on encrypted gradients. We validate Med-Q Ledger's efficacy through a critical application: the prediction of intestinal complications like necrotizing enterocolitis (NEC) in preterm infants, a condition frequently necessitating emergency colostomy. By processing physiological data from maternal wearable sensors and infant intestinal images, our integrated Random Forest model demonstrates superior performance in predicting colostomy necessity. Experimental evaluations reveal a throughput of approximately 3400 transactions per second (TPS) with ~180 ms end-to-end latency, a >95% anomaly detection rate with <2% false positives, and an 11% computational overhead for PQC on resource-constrained devices. Furthermore, our results show a 0.90 F1-score for colostomy prediction, a 25% reduction in emergency surgeries, and 31% lower energy consumption compared to MQTT baselines. Med-Q Ledger sets a new benchmark for secure, high-performance, and privacy-preserving IoMT analytics, offering a robust blueprint for next-generation healthcare deployments.
The widespread adoption of big data and AI technologies has accelerated the advancement of intelligent medical diagnostics. However, the sensitivity of medical data poses a dual challenge of privacy leakage and computational inefficiency in cross-institutional collaboration. Traditional federated learning (FL) schemes struggle to balance privacy protection, model accuracy, and communication costs, particularly for real-time processing of high-resolution medical images. To address this, we propose CZ-FLMed, a privacy-preserving FL framework integrating CKKS fully homomorphic encryption (FHE) and zkSNARKs zero-knowledge proofs. The framework employs a customized Convolutional Neural Network (CNN) for medical image training, the CKKS segmented encryption strategy for reducing communication overhead, and the lightweight Groth16 protocol for secure identity verification. This enables efficient encrypted model aggregation and authentication. The experiments results conducted in this paper on Chest X-Ray pneumonia dataset and MNIST handwritten digits demonstrate that CZ-FLMed achieves 83.05 % test accuracy in pneumonia classification. Compared to Paillier encryption, it reduces communication costs by 92.84 % and improves encryption efficiency by 404 times. Thus, the framework balances model accuracy, computational efficiency, and privacy preservation, offering a practical solution for multicenter medical collaboration.
The rapid evolution of phishing attacks targeting email, chat, and social media platforms poses a significant threat to digital security, with a reported 667% surge in spear-phishing during the 2020 COVID-19 crisis [1]. Current AI-based detection systems face challenges in dataset diversity, adversarial robustness, computational scalability, model interpretability, and privacy preservation, limiting their efficacy in real-time, multi-platform environments. This paper introduces PhishGuard, an innovative framework for real-time phishing detection, designed to overcome these limitations. PhishGuard integrates lightweight transformer models (e.g., distilled BERT), hybrid detection techniques combining natural language processing (NLP), propagation analysis, and user behavior analysis, and explainable AI (XAI) methods like SHAP and LIME for transparent decision-making. Privacy-preserving techniques, including federated learning and local differential privacy, ensure secure processing of sensitive user data. Evaluated on diverse datasets such as PhiKitA, Enron, and a custom social media corpus, PhishGuard achieves up to 97.5% accuracy, 94% F1-score, and inference times below 5 ms, demonstrating scalability for resource-constrained devices. The framework also incorporates zero-knowledge proofs for verifiable inference, addressing trust and integrity concerns. By tackling cross-domain generalization, adversarial robustness, and real-time performance, PhishGuard offers a scalable, user centric solution for secure digital communications, with applications in finance, healthcare, and social media platforms. Future enhancements include multilingual support and image based phishing detection, paving the way for a comprehensive defense against evolving cyber threats.
Cardiovascular disease (CVD) is rising as a significant concern for the healthcare sector around the world. Researchers have applied multiple traditional approaches to making healthcare systems find new solutions for the CVD concern. Artificial Intelligence (AI) and blockchain are emerging approaches that may be integrated into the healthcare sector to help responsible and secure decision-making in dealing with CVD concerns. Secure CVD information is needed while dealing with confidential patient healthcare data, especially with a decentralized blockchain technology (BCT) system that requires strong encryption. However, AI and blockchain-empowered approaches could make people trust the healthcare sector, mainly in diagnosing areas like cardiovascular care. This research proposed an explainable AI (XAI) approach entangled with BCT that enhances healthcare interpretability and responsibility to cardiovascular health medical experts. XAI is significant in addressing cardiovascular prediction issues and offers potential solutions for complex communication and decision-making in cardiovascular care. The proposed approach performs better, with the highest accuracy of 97.12% compared to earlier methods. This achievement shows its ability to tackle complex issues, accessible during healthcare sector communication and decision processes.
Open access
Artificial Intelligence in Healthcare and Education
Anna Maria Fink, Johanna Nattenmüller, Stephan Rau, Alexander Rau · 10 authors
OBJECTIVES: This study evaluated the effect of enhancing a GPT-4 model with retrieval-augmented generation on its ability to diagnose and classify traumatic injuries based on radiology reports. MATERIALS AND METHODS: In this prospective proof-of-concept study, we used retrieval-augmented generation as a zero-shot learning approach to provide expert knowledge from the RadioGraphics top ten reading list for trauma radiology to the GPT-4 model, creating the context-aware TraumaCB. Radiological report findings of 50 traumatic injuries were independently generated by two radiologists. The performance of the TraumaCB compared to the generic GPT-4 was evaluated by three board-certified radiologists, assessing the accuracy and trustworthiness of the chatbot responses in the 100 reports created. RESULTS: The TraumaCB achieved 100% correct diagnoses, 96% correct classification, and 87% correct grading, outperforming the generic GPT-4 with 93% correct diagnoses, 70% correct classification, and 48% correct grading. TraumaCB sources consistently achieved a median rating of 5.0 for explanation and trust. Challenges encountered mainly involved traumatic injuries lacking widely accepted classification systems. CONCLUSION: Augmenting a commercial GPT-4 model with retrieval-augmented generation improves its diagnostic and classification capabilities, positioning it as a valuable tool for efficiently assessing traumatic injuries across various anatomical regions in trauma radiology. KEY POINTS: Question Retrieval-augmented generation has the potential to enhance generic chatbots with task-specific knowledge of emergency radiology. Findings The TraumaCB excelled in accuracy, particularly in injury classification and grading, and provided explanations along with the sources used, increasing transparency and facilitating verification. Clinical relevance The TraumaCB provides accurate, fast, and transparent access to trauma radiology classifications, potentially increasing the efficiency of image interpretation in emergency departments and enabling customized reports based on local or individual preferences.
Open access
Artificial Intelligence in Healthcare and Education
Abstract Tuberculosis (TB) remains a global public health challenge, particularly in resource-limited settings, where centralized diagnostic systems may be constrained by logistical delays, infrastructure gaps, and limited workforce capacity. In recent years, decentralized diagnostic approaches have emerged as promising alternatives to improve access, timeliness, and patient-centered care. This narrative review examines the evolving landscape of decentralized TB diagnostics in primary laboratories, especially in the post-COVID-19 era, where pandemic-related investments in health infrastructure and digital systems have created new opportunities for diagnostic reform. We identify and synthesize five persistent implementation challenges — quality assurance, human resource limitations, infrastructural deficits, financial sustainability, and community engagement — as well as several key enablers, including portable molecular technologies, digital health platforms, and policy-linked diagnostic networks. This review highlights the importance of embedding decentralized models into national TB programs, supported by regulatory frameworks, sustainable financing, and cross-sector collaboration. Accelerating the institutionalization of decentralized diagnostics is essential to achieving diagnostic equity and advancing TB elimination goals in high-burden countries.
This paper summarises the intersection of Web3 and AI technologies, synergies between these technologies, and gaps that we suggest exist in the conception of the possible integrations of these technologies. The summary is informed by a comprehensive literature review of current academic and industry papers, analyst reports, and Ethereum research community blogposts. We focus our contribution on the perceived gaps and detail some novel approaches that would benefit the blockchain/Web3 ecosystem. We believe that the overview presented in this paper will help guide researchers interested in the intersection of Web3 and AI technologies.
The diversity and scarcity of the medical information makes it difficult to create precise global classification approach for the healthcare applications.The main motive is the privacy issue that restricts the data exchanging scope between healthcare institutions.On the contrary, an information from single source is not adequate for developing the worldwide diagnosis approach.The Federated Learning (FL) is a promising solution for privacy and data multiplicity issues, an appropriate aggregation model for multi class and dissimilar medical information is still challenging task in the recognition.Moreover, the FL approaches does not effectively analyzes the each participant execution in the local model and secures the user data.In order to overcome this issue, the Zero-Knowledge Proof (ZKP) based FL approach is developed over blockchain (BC) for performing the COVID-19 classification.The global model of FL uses the two layer Long Short Term Memory (2LLSTM) with federated proximal term (FedProx) namely 2LLSTMFP while the Convolutional Neural Network (CNN) is used in the local model.The integration ZKP and BS is used to improve the data confidentiality while the immutability of BC helps to prevent unauthorized variations for the ledger.The developed FLBC-ZKP is analyzed with two datasets such as COVID-19 Radiography, and CXR images pneumonia and COVID-19.The FLBC-ZKP is evaluated using accuracy, recall, precision, specificity, F1-score, False Negative Rate (FNR) and False Positive Rate (FPR).The existing researches such as WMT, MCCF, 3SFDL and TOTL are used to compare the FLBC-ZKP method.The FLBC-ZKP achieves improved accuracy of 98.34 % for COVID-19 Radiography dataset that is better than the MCCF and 3SFDL.
Entesar Hamed I. Eliwa, Amr Mohamed El Koshiry, Tarek Abd El‐Hafeez, Ahmed Omar
Background: The global healthcare system faces challenges in diagnosing and managing lung and colon cancers, which are significant health burdens. Traditional diagnostic methods are inefficient and prone to errors, while data privacy and security concerns persist. Objective: This study aims to develop a secure and transparent framework for remote consultation and classification of lung and colon cancer, leveraging blockchain technology and Microsoft Azure cloud services. Dataset and Features: The framework utilizes the LC25000 dataset, containing 25,000 histopathological images, for training and evaluating advanced machine learning models. Key features include secure data upload, anonymization, encryption, and controlled access via blockchain and Azure services. Methods: The proposed framework integrates Microsoft Azure’s cloud services with a permissioned blockchain network. Patients upload CT scans through a mobile app, which are then preprocessed, anonymized, and stored securely in Azure Blob Storage. Blockchain smart contracts manage data access, ensuring only authorized specialists can retrieve and analyze the scans. Azure Machine Learning is used to train and deploy state-of-the-art machine learning models for cancer classification. Evaluation Metrics: The framework’s performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, demonstrating the effectiveness of the integrated approach in enhancing diagnostic accuracy and data security. Results: The proposed framework achieves an impressive accuracy of 100% for lung and colon cancer classification using DenseNet, ResNet50, and MobileNet models with different split ratios (70–30, 80–20, 90–10). The F1-score and k-fold cross-validation accuracy (5-fold and 10-fold) also demonstrate exceptional performance, with values exceeding 99.9%. Real-time notifications and secure remote consultations enhance the efficiency and transparency of the diagnostic process, contributing to better patient outcomes and streamlined cancer care management.
Shumaiya Akter Shammi, Pronab Ghosh, Ananda Sutradhar, F. M. Javed Mehedi Shamrat · 6 authors
Modern healthcare should include artificial intelligence (AI) technologies for disease identification and monitoring, particularly for chronic conditions, including heart, diabetes, kidney, liver, and thyroid. According to the World Health Organization (WHO), heart, diabetes, and liver diseases (hepatitis B and C and liver cirrhosis) are leading causes of mortality. The prevalence of thyroid and chronic kidney diseases is also increasing. We conducted a comprehensive review of the available literature to assess the current state of AI advancement in disease diagnosis and identify areas needing further attention. Machine learning (ML), deep learning (DL), and ensemble learning (EL) approaches have gained popularity in recent years due to their excellent results across various medical domains. This study focuses on their application in disease diagnosis and monitoring. We present a framework designed to provide aspiring researchers with a foundational understanding of popular algorithms and their significance in disease identification. Additionally, we highlight the importance of blockchain technology in the healthcare industry for safeguarding patient data confidentiality and privacy. The decentralized and immutable nature of blockchain can enhance data security, promote interoperability, and empower patients to control their medical information. By demonstrating the potential of advanced ML methods and blockchain technology to transform healthcare systems and improve patient outcomes, our research contributes to the field of disease diagnostics.
Rihab Saidi, Ines Rahmany, Salah Dhahri, Tarek Moulahi
This study presents a novel approach for the early diagnosis of prevalent chest diseases, including COVID-19, pneumonia, and lung cancer, utilizing advanced machine learning techniques. The research focuses on addressing the limitations of traditional diagnostic methods by introducing Federated Learning as a collaborative and privacy-preserving solution. By leveraging Federated Learning, stakeholders can collectively develop accurate diagnostic models without directly sharing sensitive medical data, ensuring both privacy and diagnostic accuracy. Furthermore, the study proposes a multi-classification Federated Learning method enhanced by blockchain technology to reinforce data security and privacy. Experimental results demonstrate the effectiveness of this approach compared to centralized models, showcasing comparable performance in terms of accuracy and superior achievement in terms of privacy preservation. The integration of blockchain into the Federated Learning framework holds promise for a robust system prioritizing data privacy and security in the healthcare domain. This innovative combination not only advances machine learning in medical diagnostics but also sets a forward-looking approach for safeguarding patient information in today’s data-driven healthcare landscape.
In the early months of 2020, a fast-spreading outbreak was brought about by the new virus SARS-CoV-2. The uncontrolled spread, which led to a pandemic, illustrated the healthcare system’s slow response time to public health emergencies at that time. Blockchain technology was anticipated to be crucial in the effort to contain the COVID-19 pandemic. In that review, many potential blockchain applications were discovered; however, the majority of them were still in their infancy, and it couldn’t yet be predicted how they could contribute to the fight against COVID-19 through the use of platforms, access kinds, and consensus algorithms. Modern innovations such as blockchain and artificial intelligence (AI) were shown to be promising in limiting the spread of a virus. Blockchain could specifically aid in the battle against pandemics by supporting early epidemic identification, assuring the ordering of clinical information, and maintaining a trustworthy medical chain during disease tracing. AI also offered smart forms of diagnosing coronavirus therapies and supported the development of pharmaceuticals. Blockchain and AI software for epidemic and pandemic containment were analyzed in that research. First, a new conceptual strategy was proposed to tackle COVID-19 through an architecture that fused AI with blockchain. State-of-the-art research on the benefits of blockchain and AI in COVID-19 containment was then reviewed. Recent initiatives and use cases developed to tackle the coronavirus pandemic were also presented. A case study using federated intelligence for COVID-19 identification was also provided. Finally, attention was drawn to problems and prospective directions for further investigation into future coronavirus-like wide-ranging scenarios.
Open access
COVID-19 diagnosis using AI
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
The advent of smart cities has paved the way for transformative advancements in healthcare, particularly in the domain of disease diagnosis. In the wake of the COVID-19 pandemic, accurate and timely identification of Pandemic diseases has become paramount. This paper explores the challenges and opportunities in synergizing Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain technologies for diagnosis of Pandemic diseases in smart cities. This study provides an overview of each technology and its relevance to sustainable healthcare in smart cities, emphasizing its potential for analyzing medical data and making informed decisions. We also explore how IoT devices can contribute to disease surveillance, enabling real-time data collection and remote healthcare. Additionally, we discuss the potential of Blockchain in ensuring secure and transparent healthcare systems. Following, the paper study the synergistic potential of integrating AI, IoT, and blockchain, emphasizing how their combined strengths can enhance the accuracy, efficiency, and security of COVID-19 diagnosis systems in smart cities Moreover, the paper highlights the challenges in integrating these technologies and the opportunities for research and implementation, underlining the significance of synergizing AI, IoT, and Blockchain in disease diagnosis in smart cities. The findings demonstrate that the convergence of AI, IoT, and blockchain can enhance the speed and accuracy of diagnosing Pandemic diseases, leading to more effective containment and management strategies.
The healthcare industry has advanced its digitalization and use of electronic medical records during the past ten years (EMRs). The EHR system gives the information's proprietor authority over their data and allows them to communicate it to certain individuals. It is challenging for data to maintain security and diagnostic processes because of the enormous volume of data in the medical field. This research presents a novel blockchain-based encryption system using deep learning (BcEs-DLM) for secure medical data management. The concept that is being described encompasses many phases of activities, including safe data management via blockchain, encryption, and optimal key generation. It provides individuals with the ability to manage data accessibility, granting read/write access to hospital authorities, and triggering precautionary agreements. Our recommended approach offers a reliable methodology for generating secure encryption keys and effectively safeguarding sensitive medical data using the block cipher technique. By following this method, you can ensure that patient information remains confidential and protected from unauthorized access. The detection process is performed using medical record sharing. In this paper, we achieved a 97 percent accuracy after training our deep learning model. Additionally, every node of this system is registered and updated on the blockchain
The recognition and transfer of credits is becoming essential for students, as an increasing number of them are studying at different institutions, often at the same time online, in both traditional and unconventional settings. Micro-credentials can aid in this process by providing easily accessible and transparent evidence of skills or knowledge, certified by an authority, based on small units of learning. The development of blockchain technology holds promise of becoming a useful enabler for supporting the storage and dissemination of micro-credentials on a global scale. Because of its immutability, blockchain can be used to attest to students' accomplishments securely and privately. Artificial intelligence (AI) can facilitate the maintenance and dissemination of micro-credentials, while ensuring that access is readily available for students under their control. So, AI can play a role in supporting blockchain-enabled micro-credentials. For educators, a basic understanding of the development of all three technologies is becoming essential.