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<p>The amalgamation of blockchain-distributed ledger technology (BDLT) and explainable artificial intelligence (XAI) has the potential to revolutionize modern healthcare. The present research draws attention to the existing constraints on patient trust, preservation, data security and privacy, and moral judgment. Using BDLT-integrated XAI, this study offers a novel framework for developing secure, personalized, transparent, and privacy-protected healthcare decision-making. The suggested framework is assessed using several important metrics, including as interpretability accuracy, which is 92.85% in user comprehension tests. Both data security, which employs blockchain technology to verify a 98.97% success rate in limiting unauthorized access/gained access, and privacy preservation, which upholds standardization compliance to 96.78%, have an impact on user satisfaction with the general data protection regulation (GDPR). The research includes the diagnostics’ accuracy rate, which increases AI-driven diagnoses by 97.12% using blockchain-verified data. Positive evaluations of patient–physician interactions indicate that the trustworthiness rate is up to 94.33%. It seamlessly integrates with a variety of healthcare apps and achieves 99.02% platform interoperability. When compared to other state-of-the-art methods, the BDLT-powered artificial intelligence-driven refining process validates data and demonstrates an efficiency boost of up to 89.45%. The scalability and effectiveness of the proposed framework are confirmed by simulations and real-world situations. It emphasizes primarily the potential to transform healthcare through the promotion of moral behavior. With increased transparency, this paper empowers patients and improves treatment results. The findings open the door to a new era of ethical and precise medicine by establishing a secure, private, reliable, and customized approach to AI adoption in healthcare.</p>

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Leveraging blockchain-integrated explainable artificial intelligence (XAI) for ethical and personalized healthcare decision-making: a framework for secure data sharing and enhanced patient trust

  • 期刊论文

  • 发表日期：

    2025年9月18日

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  • Abdullah ayub khan,
  • Refka ghodhbani,
  • Abdulmajeed Alsufyani,
  • Nawal Alsufyani,
  • Mohamad Afendee Mohamed

摘要

The amalgamation of blockchain-distributed ledger technology (BDLT) and explainable artificial intelligence (XAI) has the potential to revolutionize modern healthcare. The present research draws attention to the existing constraints on patient trust, preservation, data security and privacy, and moral judgment. Using BDLT-integrated XAI, this study offers a novel framework for developing secure, personalized, transparent, and privacy-protected healthcare decision-making. The suggested framework is assessed using several important metrics, including as interpretability accuracy, which is 92.85% in user comprehension tests. Both data security, which employs blockchain technology to verify a 98.97% success rate in limiting unauthorized access/gained access, and privacy preservation, which upholds standardization compliance to 96.78%, have an impact on user satisfaction with the general data protection regulation (GDPR). The research includes the diagnostics’ accuracy rate, which increases AI-driven diagnoses by 97.12% using blockchain-verified data. Positive evaluations of patient–physician interactions indicate that the trustworthiness rate is up to 94.33%. It seamlessly integrates with a variety of healthcare apps and achieves 99.02% platform interoperability. When compared to other state-of-the-art methods, the BDLT-powered artificial intelligence-driven refining process validates data and demonstrates an efficiency boost of up to 89.45%. The scalability and effectiveness of the proposed framework are confirmed by simulations and real-world situations. It emphasizes primarily the potential to transform healthcare through the promotion of moral behavior. With increased transparency, this paper empowers patients and improves treatment results. The findings open the door to a new era of ethical and precise medicine by establishing a secure, private, reliable, and customized approach to AI adoption in healthcare.