Blockchain-Enabled Explainable AI for Trusted Healthcare Systems

• URL: http://arxiv.org/abs/2509.14987v1
• Date: Thu, 18 Sep 2025 14:17:19 GMT
• Title: Blockchain-Enabled Explainable AI for Trusted Healthcare Systems
• Authors: Md Talha Mohsin,
• Abstract summary: This paper introduces a-Integrated Explainable AI Framework (BXHF) for healthcare systems.<n>We tackle two challenges confronting health information networks: safe data exchange and comprehensible AI-driven clinical decision-making.<n>Our architecture incorporates blockchain, ensuring patient records are immutable, auditable, and tamper-proof.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a Blockchain-Integrated Explainable AI Framework (BXHF) for healthcare systems to tackle two essential challenges confronting health information networks: safe data exchange and comprehensible AI-driven clinical decision-making. Our architecture incorporates blockchain, ensuring patient records are immutable, auditable, and tamper-proof, alongside Explainable AI (XAI) methodologies that yield transparent and clinically relevant model predictions. By incorporating security assurances and interpretability requirements into a unified optimization pipeline, BXHF ensures both data-level trust (by verified and encrypted record sharing) and decision-level trust (with auditable and clinically aligned explanations). Its hybrid edge-cloud architecture allows for federated computation across different institutions, enabling collaborative analytics while protecting patient privacy. We demonstrate the framework's applicability through use cases such as cross-border clinical research networks, uncommon illness detection and high-risk intervention decision support. By ensuring transparency, auditability, and regulatory compliance, BXHF improves the credibility, uptake, and effectiveness of AI in healthcare, laying the groundwork for safer and more reliable clinical decision-making.

Related papers

• Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation [97.36081721024728]
  We propose the first benchmark for assessing confidence in multi-turn interaction during realistic medical consultations.<n>Our benchmark unifies three types of medical data for open-ended diagnostic generation.<n>We present MedConf, an evidence-grounded linguistic self-assessment framework.
  arXiv  Detail & Related papers  (2026-01-22T04:51:39Z)
• zkFL-Health: Blockchain-Enabled Zero-Knowledge Federated Learning for Medical AI Privacy [0.0]
  zkFL-Health is an architecture that combines Federated Learning (FL) with zero-knowledge proofs (ZKPs) and Trusted Execution Environments (TEEs)<n>Clients locally train and commit their updates; the aggregator operates within a TEE to compute the global update and produces a succinct ZK proof that it used exactly the committed inputs and the correct aggregation rule, without revealing any client update to the host.<n>We outline system and threat models tailored to healthcare, the zkFL-Health protocol, security/privacy guarantees, and a performance evaluation plan spanning accuracy, privacy risk, latency, and cost.
  arXiv  Detail & Related papers  (2025-12-24T08:29:28Z)
• Embedding Explainable AI in NHS Clinical Safety: The Explainability-Enabled Clinical Safety Framework (ECSF) [0.0]
  This paper proposes an Explainability-Enabled Clinical Safety Framework (ECSF) that integrates explainability into the DCB0129/0160 lifecycle.<n>Cross-regulatory synthesis maps DCB clauses to principles from Good Machine Learning Practice, the NHS AI Assurance and T.E.S.T frameworks, and the EU AI Act.<n>ECSF introduces five checkpoints: global transparency for hazard identification, case-level interpretability for verification, clinician usability for evaluation, traceable decision pathways for risk control, and longitudinal interpretability monitoring for post-market surveillance.
  arXiv  Detail & Related papers  (2025-10-24T07:36:58Z)
• Fusion of Machine Learning and Blockchain-based Privacy-Preserving Approach for Health Care Data in the Internet of Things [0.5097809301149341]
  The rapid integration of Internet of Things (IoT) devices into the healthcare sector has brought about revolutionary advancements in patient care and data management.<n>These technological innovations raise critical security concerns, particularly in safeguarding medical data against potential cyber threats.<n>We propose a comprehensive method encompassing three distinct phases to address the imperative need for enhanced security in IoT-based healthcare systems.
  arXiv  Detail & Related papers  (2025-10-21T19:09:46Z)
• Quantum-Inspired Privacy-Preserving Federated Learning Framework for Secure Dementia Classification [0.0]
  This paper introduces a novel framework that integrates federated learning with quantum-inspired encryption techniques for dementia classification.<n>The framework offers significant implications for democratizing access to AI-driven dementia diagnostics in low- and middle-income countries.
  arXiv  Detail & Related papers  (2025-03-05T08:49:31Z)
• Artificial Intelligence-Driven Clinical Decision Support Systems [5.010570270212569]
  The chapter emphasizes that creating trustworthy AI systems in healthcare requires careful consideration of fairness, explainability, and privacy.<n>The challenge of ensuring equitable healthcare delivery through AI is stressed, discussing methods to identify and mitigate bias in clinical predictive models.<n>The discussion advances in an analysis of privacy vulnerabilities in medical AI systems, from data leakage in deep learning models to sophisticated attacks against model explanations.
  arXiv  Detail & Related papers  (2025-01-16T16:17:39Z)
• Balancing Confidentiality and Transparency for Blockchain-based Process-Aware Information Systems [43.253676241213626]
  We propose an architecture for blockchain-based PAISs to preserve confidentiality and transparency.<n>Smart contracts enact, enforce and store public interactions, while attribute-based encryption techniques are adopted to specify access grants to confidential information.<n>We assess the security of our solution through a systematic threat model analysis and evaluate its practical feasibility.
  arXiv  Detail & Related papers  (2024-12-07T20:18:36Z)
• Meta-Sealing: A Revolutionizing Integrity Assurance Protocol for Transparent, Tamper-Proof, and Trustworthy AI System [0.0]
  This research introduces Meta-Sealing, a cryptographic framework that fundamentally changes integrity verification in AI systems. The framework combines advanced cryptography with distributed verification, delivering tamper-evident guarantees that achieve both mathematical rigor and computational efficiency.
  arXiv  Detail & Related papers  (2024-10-31T15:31:22Z)
• Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering [51.26412822853409]
  We present a novel personalized federated learning (pFL) method for medical visual question answering (VQA) models. Our method introduces learnable prompts into a Transformer architecture to efficiently train it on diverse medical datasets without massive computational costs.
  arXiv  Detail & Related papers  (2024-10-23T00:31:17Z)
• Beyond One-Time Validation: A Framework for Adaptive Validation of Prognostic and Diagnostic AI-based Medical Devices [55.319842359034546]
  Existing approaches often fall short in addressing the complexity of practically deploying these devices. The presented framework emphasizes the importance of repeating validation and fine-tuning during deployment. It is positioned within the current US and EU regulatory landscapes.
  arXiv  Detail & Related papers  (2024-09-07T11:13:52Z)
• A Scalable Multi-Layered Blockchain Architecture for Enhanced EHR Sharing and Drug Supply Chain Management [3.149883354098941]
  This paper presents a scalable, multi-layered blockchain architecture for secure Electronic Health Record sharing and drug supply chain management.<n>The proposed framework introduces five distinct layers that enhance system performance, security, and patient-centric access control.<n>Our solution ensures data integrity, privacy, and interoperability, making it compatible with existing healthcare systems.
  arXiv  Detail & Related papers  (2024-02-27T09:20:16Z)
• Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
  We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
  arXiv  Detail & Related papers  (2023-07-29T12:54:03Z)
• Privacy-preserving medical image analysis [53.4844489668116]
  We present PriMIA, a software framework designed for privacy-preserving machine learning (PPML) in medical imaging. We show significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets. We empirically evaluate the framework's security against a gradient-based model inversion attack.
  arXiv  Detail & Related papers  (2020-12-10T13:56:00Z)
• Trustworthy AI [75.99046162669997]
  Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations. We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
  arXiv  Detail & Related papers  (2020-11-02T20:04:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.