Blockchain Papers

Duc Thinh Ngo, Arya Rahgoza

Systematic reviews are essential for evidence-based medicine, but reviewing 1.5 million+ annual publications manually is infeasible. Current AI approaches suffer from hallucinations in systematic review tasks, with studies reporting rates ranging from 28--40% for earlier models to 2--15% for modern implementations which is unacceptable when errors impact patient care. We present a causal graph-enhanced retrieval-augmented generation system integrating explicit causal reasoning with dual-level knowledge graphs. Our approach enforces evidence-first protocols where every causal claim traces to retrieved literature and automatically generates directed acyclic graphs visualizing intervention-outcome pathways. Evaluation on 234 dementia exercise abstracts shows CausalAgent achieves 95% accuracy, 100% retrieval success, and zero hallucinations versus 34% accuracy and 10% hallucinations for baseline AI. Automatic causal graphs enable explicit mechanism modeling, visual synthesis, and enhanced interpretability. While this proof-of-concept evaluation used ten questions focused on dementia exercise research, the architectural approach demonstrates transferable principles for trustworthy medical AI and causal reasoning's potential for high-stakes healthcare.

Heath Emerson

Large language models and agentic AI systems deployed in regulated, safety-critical, and high-stakes enterprise environments require governance infrastructure that is simultaneously cryptographically verifiable, regulatorily defensible, operationally efficient, and natively explainable. Existing approaches treat these properties as separate concerns addressed by separate toolchains. The result is an accountability architecture that is fragmented, difficult to audit end-to-end, and structurally incapable of satisfying the converging global regulatory requirement that AI decisions be not merely governed but explainable. This paper presents THEMIS-xAI (Trusted High-Assurance Evidence Management Integrity System for Explainable AI): a unified, governance-native framework that integrates cryptographic evidence management, runtime policy enforcement, explainability generation, privacy-preserving verification, and continuous compliance monitoring into a single coherent architecture. THEMIS-xAI is organized around four architectural planes-Evidence, Control, Security, and Explainability-and eleven integrated subsystems. We demonstrate that THEMIS-xAI achieves 83% coverage of the NIST AI RMF 1.0 control set (advancing from a 72% baseline), provides architectural coverage of fifteen regulatory frameworks with per-control status disclosure, and produces per-decision explanation artifacts that are cryptographically anchored, independently verifiable, and structured to align with the transparency and documentation goals of applicable AI governance frameworks. Legal sufficiency requires independent regulatory assessment. Implementation status is transparent throughout: the Evidence and Control Planes are in active enterprise pilot deployment; the Explainability Plane modules M1-M4 are implemented; M5-M6 are at research-prototype stage; zero-knowledge enforcement proofs are at proof-of-concept stage with production hardening planned in Phase 4.

Jean Claude Niyokwizerwa

Large Language Models (LLMs) such as ChatGPT, Claude, and Gemini are being adopted across critical sectors including healthcare, government services, financial services, and education. Yet these systems operate without any verified understanding of who is interacting with them. Identity is selfdeclared, guardrails exist only at the prompt layer, and every session begins from zero. This paper proposes a novel architectural framework, the Verified Credential Session Binding (VCSB) Protocol, for cryptographically binding verified digital identity credentials to LLM inference sessions. Drawing on established standards including W3C Verifiable Credentials, OpenID for Verifiable Credential Issuance (OID4VCI), the Open Standards Identity API (OSIA), and zero-knowledge proof primitives, the framework enables attribute-based policy enforcement upstream of the model without compromising user privacy. The paper presents the theoretical foundation, a concrete technical specification, a governance model for national deployment, and an illustrative implementation grounded in Rwanda's E-Ndangamuntu Single Digital Identity system. The proposed protocol addresses a fundamental gap in responsible AI deployment and positions national digital identity infrastructure as a critical enabler of trustworthy AI governance.

Samir Lotfi Ali

Abstract Scientific knowledge is communicated through claims whose validity depends on assumptions, methods, scope, and environmental conditions. Yet these validity conditions are progressively compressed or lost as knowledge moves through publication, citation, education, policy, and operational deployment. The result is that knowledge is frequently applied outside the domain within which it was established, while the boundary crossing itself remains invisible. This paper proposes epistemic conditionality as a general diagnostic framework for consequential knowledge systems. It introduces Law Zero (the Law of Epistemic Conditional Validity), which states that every consequential knowledge claim is valid only within an explicitly declarable validity domain, D_M = A_R ∩ M_R ∩ S_R ∩ E_R, where the four dimensions represent foundational assumptions, methodological constraints, scope, and environmental conditions. The paper argues that this four-dimensional architecture constitutes the minimal logical structure required to specify the validity domain of consequential knowledge claims, while the domain-specific content of each dimension remains the responsibility of individual scientific communities. Building upon this foundation, the paper introduces the Universal Scientific Domain Address as a formal representation of validity domains and identifies the Epistemic Banality of Science as the structural mechanism through which validity conditions are progressively stripped during scientific dissemination. Four formal principles are derived from this framework, together with a Construct Stripping Consequence for bounded measurement constructs. Nine documented cases spanning mathematics, clinical medicine, AI procurement, engineering, and historical methodology demonstrate the stripping mechanism across the dissemination chain. The framework is illustrated through five cross-domain demonstrations spanning artificial intelligence, autonomous systems, human education, historical practice, and philosophy as epistemic foundation. These demonstrations are presented as illustrative applications of the diagnostic lens rather than as proofs of universality, and are intended to motivate subsequent empirical, computational, and domain-specific research. Finally, the paper proposes a transition from descriptive scientific communication toward addressed scientific communication, in which consequential knowledge claims carry machine-readable validity metadata capable of supporting both cross-disciplinary governance and future human-machine symbiotic decision systems. This proposal is presented as a research programme and invitation to scientific communities rather than as an established standard. Keywords: Epistemic Conditionality, Universal Scientific Domain Address, Law Zero, Epistemic Banality of Science, Validity Domain, Validity Domain Violations, Context Stripping, Epistemic Governance, Scientific Addressing, Construct Stripping, AI Hallucination, Operational Design Domain, Human-AI Symbiosis, Historical Epistemology, Philosophy of Knowledge, Straw Man Fallacy, Semantic Grounding, Man-Machine Symbiotic Governance, Construct Validity, USIS, Universal Scientific Addressing System

Francesco Saverio Canepa

Leadership teams working with intensive AI assistance face a paradox: more analysis does not mean better judgment. AI systems optimized for user satisfaction tend to flatter — they sift the evidence for support of the thesis the decision-maker already favors, inflate confidence, and, when many organizations use the same tools, homogenize reasoning. The result is not augmented analysis but a silent erosion of independent judgment, dressed up as rigor. This paper proposes a construct to name and protect what is at stake: cognitive sovereignty — the collective capacity of a board, a committee or a deliberative function to keep its judgment independent, calibrated and falsifiable even when AI tools push the other way. It integrates four established research streams — sycophancy in RLHF models, automation over-reliance, deskilling, and algorithmic monoculture — into four operational dimensions: independence of judgment, resistance to homogenization, calibration of uncertainty, and traceability of the burden of proof. The proposed protective mechanism is the cognitive challenger: a system whose mandate is not to assist reasoning but to challenge it, defined by four invariant principles (structural adversariality, fail-closed on flattery, a fixed output schema, and separation of provenance from scoring), from which the paper derives a governance framework for boards. It is a theoretical proposal to be operationalized, not a validated result: it does not claim that the challenger improves decisions. Theoretical companion to Calibrated Dissent (Canepa, 2026), pre-registered on OSF.

Hend Abdul Ameer Hadi, Rana Fareed Ghani

Communication and information technologies have facilitated the rapid adoption of electronic medical records, leading to patient privacy and data security concerns. Blockchain technology offers a promising solution to address these issues. However, scalability remains a significant challenge for blockchain-based electronic health records (EHR) systems. In this study, we aimed to develop and evaluate an EHR management system based on blockchain technology. Therefore, we propose a management model based on organizations and user roles and implemented it using Hyperledger Fabric and the InterPlanetary File System (IPFS). The blockchain consists of three channels: one for patient registration and EHR retrieval and two additional channels dedicated to two hospitals for storing patients’ EHRs. A scalable multichain e-health system using the Hyperledger Fabric platform provides a practical option to address scalability issues and protect patients’ privacy, security, and medical data. The proposed model uses IPFS to store medical images and generate hash values, which are then stored in the blockchain. The system was evaluated using Hyperledger Explorer and Hyperledger Caliper, focusing on several performance metrics: transactions per hour, transactions per minute, blocks per hour, blocks per minute, response time, maximum latency, minimum latency, average latency, throughput, CPU and memory usage, and runtime. A comparative analysis was conducted against single-ledger EHR systems to assess the proposed system’s performance. The Hyperledger Caliper report shows that the average latency for each organization ranges from 0.11 to 0.55, and the throughput ranges from 24.2 to 200 for 1000 assets at sending rates of 25, 50, 100, and 200.

Wa Ode Siti Nur Alam, Riri Fitri Sari

The accurate and timely classification of toddlers' nutritional status is critical for early intervention, particularly in remote or underserved communities with limited access to healthcare professionals. However, data security, especially for children's health data, is equally essential to ensure safe storage and access. To address these challenges, this study proposes a hybrid AI-powered chatbot that integrates ensemble learning, blockchain, and decentralized storage to support both nutritional status classification and educational interaction. The system combines a random forest model for classification with GPT-3.5 Turbo for bilingual (Indonesian–English) stunting education deployed via Telegram. Preprocessing includes standardizing, normalizing, and encoding Indonesian-language nutrition data to ensure machine learning readiness. Six ensemble algorithms are evaluated using stratified five-fold cross-validation, with classification results hashed using SHA-256 and immutably stored on the Interplanetary File System (IPFS) and a local Ethereum blockchain. The chatbot effectively manages both structured inputs and natural language queries, ensuring secure, transparent, and real-time nutritional assessments. Results demonstrate high classification performance, with the random forest model achieving the highest mean F1-score (0.9987) and the lowest deviation. Its robustness was validated by a 20% hold-out test set and stratified five-fold cross-validation, which obtained excellent balanced performance across nutritional status categories (F1-macro, precision, recall, accuracy ≈ 0.99; ROC AUC = 1.00). External validation also yielded robust and consistent results (F1-macro = 0.97, precision = 0.97, recall = 0.96, ROC AUC = 0.98, and accuracy = 0.97), demonstrating the model's generalization ability and mitigating concerns regarding overfitting. Blockchain evaluation confirmed stable and linear CID transaction throughput (blocks 29–46) with no observed latency, ensuring reliable and continuous data recording. Furthermore, gas prices decreased by ~87.5%, highlighting significant improvements in cost efficiency and scalability, which reinforces blockchain's feasibility for decentralized, AI-driven health data management. Received: 9 June 2025 | Revised: 29 September 2025 | Accepted: 31 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/rendiputra/stunting-balita-detection-121k-rows and https://www.kaggle.com/datasets/jabirmuktabir/stunting-wasting-dataset. Author Contribution Statement Wa Ode Siti Nur Alam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Riri Fitri Sari: Conceptualization, Writing – review & editing, Supervision, Funding acquisition.

Shrutika Singh, Anton Alyakin, Daniel Alexander Alber, Jaden Stryker · 12 authors

The performance of Large Language Models (LLMs) on multiple-choice question (MCQ) benchmarks is frequently cited as proof of their medical capabilities. We hypothesized that LLM performance on medical MCQs may in part be illusory and driven by factors beyond medical content knowledge and reasoning capabilities. To assess this, we created a novel benchmark of free-response questions with paired MCQs (FreeMedQA). Using this benchmark, we evaluated three state-of-the-art LLMs (GPT-4o, GPT-3.5, and LLama-3-70B-instruct) and found an average absolute deterioration of 39.43% in performance on free-response questions relative to multiple-choice (p = 1.3 * 10 -5 ) which was greater than the human performance decline of 22.29%. To isolate the role of the MCQ format on performance, we performed a masking study, iteratively masking out parts of the question stem. At 100% masking, the average LLM multiple-choice performance was 6.70% greater than random chance (p = 0.002) with one LLM (GPT-4o) obtaining an accuracy of 37.34%. Notably, for all LLMs the free-response performance was near zero. Our results highlight the shortcomings in medical MCQ benchmarks for overestimating the capabilities of LLMs in medicine, and, broadly, the potential for improving both human and machine assessments using LLM-evaluated free-response questions.

Mohamed Ezz, Alaa Alaerjan, Ayman Mohamed Mostafa

In today's rapidly advancing healthcare landscape, integrating Artificial Intelligence (AI) and Machine Learning (ML) has the potential to significantly improve patient care and streamline medical processes. The utilization of confidential patient data to train and develop these technologies, however, raises significant concerns regarding authenticity, security, and privacy. In this study, we introduce MediChainAI, a safe and practical framework that allows patients full ownership over their own health data by integrating Self-Sovereign Identity (SSI), Blockchain, and sophisticated cryptography techniques. By clearly outlining the goals and parameters of this access, MediChainAI allows patients to safely and selectively share data with healthcare providers and researchers. While SSI guarantees that patients have ownership of their data, the framework uses Blockchain technology to keep things transparent and secure. Further, MediChainAI makes use of Merkle trees, which provide verified access to subsets of data without jeopardizing the privacy of the whole dataset. The encryption mechanism, which is based on smart contracts, is a distinctive feature of the framework that allows researchers and medical practitioners controlled and secure access to patient data. In order to improve the accuracy and reliability of medical diagnoses and treatment, this strategy makes sure that only confirmed, legitimate data is utilized to train medical models. A significant step toward safer and more personalized healthcare, MediChainAI encourages ethical and patient-focused innovation by effectively resolving essential issues regarding data security and patient privacy.

Akimoto, Hitoshi

The ethical tension surrounding AI-generated art often arises from misconceptions that anthropomorphize the algorithmic process. The accusation that “AI steals human creativity” overlooks the mediating role of human design and data literacy. This paper reframes the debate as a problem of informational asymmetry rather than morality. It proposes that Non-Fungible Tokens (NFTs) and Digital Object Identifiers (DOIs) can visualize and authenticate the flow of creative tension within a transparent ecosystem. NFTs serve as formal anchors—recording authorship, signature, and temporal origin—while DOIs preserve the conceptual framework and creative process. When linked, these two systems transform authorship into a traceable circulation of knowledge, allowing the boundary between plagiarism, homage, and originality to be objectively determined. This dual-layer provenance model presents an ethical infrastructure for creation in the age of generative AI.

Gianmarco Di Palma, Roberto Scendoni, Davide Ferorelli, Anna De Benedictis · 6 authors

Background/Objectives: Artificial intelligence (AI) is revolutionizing the healthcare industry, improving diagnoses, treatments, and clinical processes. However, its integration poses significant cybersecurity risks, including data breaches, algorithmic opacity, and vulnerabilities in AI-controlled medical devices. This narrative review analyzes these threats and evaluates blockchain technology as a potential mitigation strategy within a Clinical Risk Management framework. Methods: The literature search was conducted on PubMed, Scopus, and Web of Science, considering peer-reviewed publications from 2000 to January 2025. 1,204 articles were identified. Inclusion criteria included studies on cybersecurity risks in healthcare, blockchain applications in the clinical setting, and regulatory references (eg, General Data Protection Regulation). Conference abstracts, non-English articles, and non-peer-reviewed contributions were excluded. To ensure methodological rigor, the Scale for the Assessment of Narrative Review Articles criteria were applied. Results: The thematic analysis highlighted recurring critical issues: difficulties with informed consent, unauthorized access to sensitive data, and systemic vulnerabilities in hospital digital infrastructures. Blockchain presents a promising solution thanks to its decentralization, immutability, and transparency. Integration with smart contracts enables dynamic consent management, secure data sharing, and real-time monitoring of medical devices. Permissioned networks improve traceability and regulatory compliance, while Layer 2 solutions and optimized consent protocols address scalability challenges. Conclusion: Despite its potential, blockchain adoption faces obstacles: high costs, regulatory rigidity, and poor acceptance among healthcare professionals. The review highlights the need for pilot projects, interdisciplinary collaboration, and regulatory updates for effective integration. Combining AI and blockchain in Clinical Risk Management can transform clinical risk management from reactive to proactive, improving patient safety, data governance, and accountability.

Lilia Tightiz, Abdulkhamidov Akbarjon Sobitkhon Ughli, Joon Yoo

This paper presents a novel health data analysis platform for improved individualized risk forecasting of permanent pacemaker implantation (PPI) following transcatheter aortic valve replacement (TAVR) procedures. Specifically, we introduce XGBoost-federated adaptive interpolation transfer learning (XG-FedAIT)—a platform that integrates heterogeneous hospital data sets via adaptive output-level interpolation and performance-weighted model ensembling. This approach facilitates federated learning across institutions with different feature spaces and prediction targets, such as time-to-event and binary models, eliminating structural and semantic mismatches prevalent in regular federated transfer learning. To ensure secure, privacy-preserving, and general data protection regulation (GDPR)-compliant data exchange, we propose a dual-chain blockchain architecture integrating proof of authority (PoA), zero-knowledge proofs (ZKPs), and chain-specific smart contracts, and off-chain encrypted storage through Filecoin. Experimental evaluation proves that the PrimaryChain handles over 300 tx/s with a median latency of 500 ms, and the SecondaryChain offers 99.8% data availability and decentralized access control. The system is scalable to handle up to 2,500 transactions/hour, and the federated learning pipeline classifies PPI risk with an F1-score of 0.85 and AUROC of 0.91. These results support the effectiveness of our system in delivering real-time, regulation-compliant, and clinically actionable cardiac care in distributed environments.

Seid Abdu

We present a graphics processing unit (GPU)-accelerated Proof-of-Work (PoW) blockchain design tailored for secure healthcare data management. Our Compute Unified Device Architecture (CUDA)-optimized PoW achieves throughput improvements of approximately 5× to 100× and reduces block-formation latency compared to Central Processing Unit (CPU) mining, making blockchain practical for high-volume health records. We benchmark against standard platforms-Bitcoin, known for its robust security but slow block times; Ethereum (legacy PoW), widely adopted yet less efficient; and Hyperledger Fabric, a permissioned enterprise framework-to quantify performance gains. Empirical tests show GPU-Advanced Encryption Standard in Counter Mode (AES-CTR) processes large health-record payloads in under one second, while our PoW mining throughput improves by approximately 5×, to 100× relative to unaccelerated baselines. We also evaluate end-to-end encryption latency and discuss privacy trade-offs, including that lightweight Advanced Encryption Standard (AES) yields minimal delay, whereas fully homomorphic methods, although privacy-preserving, remain impractical for real-time permissionless blockchains and are not included in our design. We explicitly address regulatory compliance: personal health data are stored off-chain (e.g., Interplanetary File System [IPFS]), preserving the "right to erasure" via deletion of off-chain records, and we implement strict access controls to meet Health Insurance Portability and Accountability Act (HIPAA) security rules. The design includes validator selection rules that limit Sybil attacks by requiring costly work (or stake) and supports post-quantum cryptographic agility (e.g., Falcon signatures). We define our research question ("Can CUDA-accelerated PoW enable a high-performance yet compliant health data blockchain?") and hypothesize that GPU parallelism will yield substantial increases in speed. Results confirm our hypothesis: throughput and latency are significantly improved while preserving data privacy and compliance. This work makes a comprehensive contribution by detailing implementation methods, performance benchmarking, and analysis of security and legal requirements in a unified blockchain framework for healthcare.

Samia Sayed, Muammar Shahrear Famous, Rashed Mazumder, Risala Tasin Khan · 8 authors

Blockchain is an emerging technology that is being used to create innovative solutions in many areas, including healthcare. Nowadays healthcare systems face challenges, especially with security, trust, and remote data access. As patient records are digitized and medical systems become more interconnected, the risk of sensitive data being exposed to cyber threats has grown. In this evolving time for healthcare, it is important to find a balance between the advantages of new technology and the protection of patient information. The combination of blockchain–InterPlanetary File System technology and conventional electronic health record (EHR) management has the potential to transform the healthcare industry by enhancing data security, interoperability, and transparency. However, a major issue that still exists in traditional healthcare systems is the continuous problem of remote data unavailability. This research examines practical methods for safely accessing patient data from any location at any time, with a special focus on IPFS servers and blockchain technology in addition to group signature encryption. Essential processes like maintaining the confidentiality of medical records and safe data transmission could be made easier by these technologies. Our proposed framework enables secure, remote access to patient data while preserving accessibility, integrity, and confidentiality using Ethereum blockchain, IPFS, and group signature encryption, demonstrating hospital-scale scalability and efficiency. Experiments show predictable throughput reduction with file size (200 → 90 tps), controlled latency growth (90 → 200 ms), and moderate gas increase (85k → 98k), confirming scalability and efficiency under varying healthcare workloads. Unlike prior blockchain–IPFS–encryption frameworks, our system demonstrates hospital-scale feasibility through the practical integration of group signatures, hierarchical key management, and off-chain erasure compliance. This design enables scalable anonymous authentication, immediate blocking of compromised credentials, and efficient key rotation without costly re-encryption.

Oyegoke Oyebode

The deployment of artificial intelligence in healthcare is increasingly constrained by privacy, equity, and regulatory compliance challenges, especially in multilingual and cross-border contexts.Traditional centralized machine learning approaches are limited by restrictions on patient data sharing, raising both ethical and legal concerns.Federated learning offers a promising solution by enabling distributed training across institutions without transferring raw data, yet ensuring trust and privacy in federated systems remains a critical barrier.This study proposes a novel framework that combines transformer architectures with encrypted federated datasets anchored by blockchain zero-knowledge proofs (ZKPs) to achieve privacy-preserving, equitable, and multilingual healthcare diagnostics.Transformer-based models, known for their strength in natural language processing and multimodal learning, are adapted to operate on encrypted federated datasets spanning diverse linguistic and demographic contexts.Blockchain provides a decentralized trust layer, while zero-knowledge proofs ensure verifiable model updates without exposing sensitive patient information.This combination allows healthcare providers to collaboratively train diagnostic models that maintain strong predictive performance while adhering to strict privacy guarantees.The framework also advances health equity by enabling multilingual diagnostics that address disparities in underrepresented populations.By integrating explainability mechanisms, stakeholders gain insights into model reasoning across diverse cultural and linguistic datasets.Case applications in federated medical imaging, multilingual clinical notes, and genomic diagnostics highlight the framework's capacity to balance accuracy, privacy, and fairness.Overall, the integration of transformers, federated learning, and blockchain ZKPs represents a pathway toward trustworthy and equitable AI-driven healthcare, enabling collaborative innovation while safeguarding patient rights.

Irum Matloob, Shoab Ahmed Khan, Bushra Bashir, Rukaiya Rukaiya · 6 authors

Healthcare recommendations and insurance have recently been one of the most emerging research areas in health informatics. The fraud in health insurance is becoming increasingly common day by day. To handle healthcare insurance fraud, there is an urgent need for an intelligent system that cannot only identify and monitor doctors' and hospitals' behavior regarding the health services they provide to patients but can also recommend doctors and hospitals to insured employees based on the quality of services they provided previously. This system creates patient and doctor profiles separately, based on their rating. The proposed system combines singular value decomposition (SVD), K-nearest neighbors based collaborative filtering (KNN-based CF), item-based collaborative filtering (Item-based CF), content-based filtering using term frequency-inverse document frequency (TF-IDF), and K-means clustering and probability distributions to recommend doctors and insurance plans. The system measures similarity scores between patients and doctors using cosine similarity, which helps to determine similarity scores and refine the recommendations. This study also uses blockchain technology to automate insurance claims reimbursement. The results are validated using real data from the employees of a local hospital. The system provides recommendations with a root mean square error (RMSE) value of 0.478 and a mean absolute error (MAE) value of 0.0422. The insurance plans developed using the proposed system have reduced the overall expenditure of the local hospital, with a reduction in total expenses. Blockchain technology further helps prevent healthcare fraud. In the proposed system, a healthcare insurance claims reimbursement system is built using smart contract technology on the Ethereum blockchain, ensuring security & transparency and lowering the number of healthcare frauds. The system includes roles for the insurance company, healthcare provider, and patients. It also provides a platform for claim submission, approval, or refusal. In Pakistan, no such system existed before recommending doctors from different hospitals based on their professional conduct or the good health services they provide.

Aleksandar Stankovic, Marina Marjanović

Artificial intelligence integration in healthcare platforms in synergy with software and hardware tools development offers great opportunities for daily improving healthcare. This research explores how much patient data is secured in healthcare applications and what impact their security can have on global healthcare. Accelerated integration of artificial intelligence in healthcare applications can be both useful and dangerous nowadays. Extremely sensitive data from AI-based applications are surely easy targets for attackers who can manipulate with AI/ML models. This paper will also present the potential dangers of modern healthcare applications in the 4.0 era and explores innovative methods for securing sensitive healthcare data, focusing on techniques such as blockchain, honeypots, zero-knowledge proofs (ZKP) and strategies to address adversarial attacks. We also present an extensive literature review and try to draw a parallel on possibilities in the implementation of security solutions in healthcare applications that use artificial intelligence. Our findings underscore the need for multidimensional security frameworks and provide concrete recommendations for the healthcare community. Ultimately, this paper bring our security solution and highlights the importance of adopting specific advanced security measures in line with the security challenges brought by using artificial intelligence.

Tanisha Bhardwaj, K Sumangali

With the rapid growth of healthcare data and the need for secure, interpretable, and decentralized machine learning systems, Federated Learning (FL) has emerged as a promising solution. However, FL models often face challenges regarding privacy preservation, transparency, and resistance to adversarial attacks. To address these limitations, this paper proposes the Privacy Preserving Federated Blockchain Explainable Artificial Intelligence Optimization (PPFBXAIO) framework, which integrates blockchain technology, Explainable AI (XAI), and optimization techniques to ensure privacy, traceability, and robustness in FL-based systems. PPFBXAIO employs Secure Hash Algorithm 256 (SHA-256) for blockchain-backed secure model updates, Min-Max normalization for feature scaling, and the Levy Grasshopper Optimization Algorithm (LGOA) for optimal feature selection and federated model tuning. The Entropy Deep Belief Network (EDBN) is used as the classifier to enhance classification accuracy and detect attacks. XAI tools like SHAP are utilized to improve model interpretability. Experimental validation was conducted using the Heart Disease dataset from Kaggle and the Wisconsin Breast Cancer dataset. Results showed that PPFBXAIO achieved 95.07% accuracy, 95.44% precision, 96.54% recall, 95.98% F1 score, and reduced training loss by 4.93% for Breast Cancer Wisconsin and achieved 93.07% accuracy, 91.19% precision, 95.39% recall, 93.24% F1 score for Heart Disease dataset. Proposed system has reduced latency by 81 ms, and improved throughput by 109 transactions per second for 100 rounds as compared to traditional models like FedAvg, FL-MPC, FL-RAEC, and PEFL. These results highlight the framework's superior performance, privacy preservation, and practical applicability in decentralized healthcare AI systems.

Dept of ECE IARE, Dadi Jagan Goud

Cryptocurrency price prediction has become crucial for informed trading decisions due to the volatile nature of assets like Bitcoin, Ethereum, Ripple, and Litecoin. Traditional methods like ARIMA and GARCH struggle with this volatility, while modern approaches such as machine learning and deep learning provide better accuracy. This study evaluates advanced models, including LSTM, GRU, and Light GBM, to predict cryptocurrency prices and assess trading strategies before and after the COVID-19 pandemic. GRU and LSTM excel at identifying patterns in price data, with GRU performing best for Ripple. Ensemble methods like Light GBM proved highly accurate for Bitcoin and Ethereum across time periods. Simpler models like RNN were sufficient for Ripple and Litecoin. The COVID-19 pandemic significantly impacted market dynamics, emphasizing the importance of precise predictions. Trading strategies based on model predictions showed that ensemble methods like Light GBM yielded the highest profitability post-pandemic. The findings highlight the need to tailor models to specific cryptocurrencies and market conditions. Improved deep learning tools can enhance trading efficiency and provide actionable insights for investors and policymakers. Future research could focus on predicting multiple cryptocurrencies simultaneously and optimizing portfolio-based trading strategies. Key Words: LSTM, ARIMA, GARCH, RNN

Shilpa Kottapally, Sr. Software Development Engineer, Adjudication - Rxclaim developement Application, CVS Health, 2100 E lake cook road , Buffalo grove Illinois 60047, USA

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Fausto Neri da Silva Vanin, Rodrigo da Rosa Righi, Cristiano André da Costa

Blockchain technology in healthcare is gaining attention for addressing data privacy, interoperability, and health record integrity issues. Standards like HL7 FHIR and OpenEHR ensure data consistency, but privacy concerns persist under regulations like HIPAA, GDPR, and LGPD. Existing methods often store only data hashes, raising validation risks. The MEPCA model introduces a blockchain-based framework for secure health record management, focusing on on-chain EHR data processing. Key elements include Data Steward, Shared Data Vault, and Zero-Knowledge Proofs of HL7 FHIR fields. Experiments with Fully Homomorphic Encryption show enhanced security and reliability for health records, offering a robust alternative to traditional off-chain approaches.

Alexandra Vultureanu‐Albişi, Costin Bădică, Mirjana Ivanović

The Internet of Things (IoT) paradigm is evolving and the Next-Generation IoT (NG-IoT) ecosystem will incorporate distributed ledger and blockchain technology, AI-adapted components, and intelligent edge solutions that take advantage of edge computing, Artificial Intelligence (AI), networks, and communications. In addition to the low integration of eXplainable Artificial Intelligence (XAI) in the IoT or NG-IoT contexts, the explainability of these systems is rarely evaluated. Due to these limitations, we thoroughly examined the current state of XAI integration with IoT services. We propose a new conceptual framework called eXING-IoT (eXplainability Integrated in the Next Generation IoT) for better NG-IoT systems' explainability integration and evaluation. This includes a list of qualities that future NG-IoT environments should have, thus paving the way for the advancement of NG-IoT beyond the state of the art.

Salman Muneer, Sagheer Abbas, Asghar Ali Shah, Meshal Alharbi · 8 authors

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.

Gowthami Jaganathan, Shanthi Natesan

In the modern era of digitalization, integration with blockchain and machine learning (ML) technologies is most important for improving applications in healthcare management and secure prediction analysis of health data. This research aims to develop a novel methodology for securely storing patient medical data and analyzing it for PCOS prediction. The main goals are to leverage Hyperledger Fabric for immutable, private data and to integrate Explainable Artificial Intelligence (XAI) techniques to enhance transparency in decision-making. The innovation of this study is the unique integration of blockchain technology with ML and XAI, solving critical issues of data security and model interpretability in healthcare. With the Caliper tool, the Hyperledger Fabric blockchain's performance is evaluated and enhanced. The suggested Explainable AI-based blockchain system for Polycystic Ovary Syndrome detection (EAIBS-PCOS) system demonstrates outstanding performance and records 98% accuracy, 100% precision, 98.04% recall, and a resultant F1-score of 99.01%. Such quantitative measures ensure the success of the proposed methodology in delivering dependable and intelligible predictions for PCOS diagnosis, therefore making a great addition to the literature while serving as a solid solution for healthcare applications in the near future.