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Data security

Revolutionizing Healthcare with Blockchain-Integrated Explainable AI

Strategic Insights

— 1 min read

Research from Bahria University has introduced a novel framework for personalized medicine, leveraging the potential of blockchain-distributed ledger technology (BDLT) and explainable artificial intelligence (XAI). The study highlights the constraints on patient trust, data security, and privacy in modern healthcare. By integrating BDLT with XAI, the researchers have developed a secure, transparent, and privacy-protected framework for healthcare decision-making. The framework boasts several key metrics, including a 92.85% interpretability accuracy rate, a 98.97% success rate in limiting unauthorized access, and a 96.78% compliance rate with data protection regulations.

Key Takeaways:

  • The study proposes a novel framework for developing secure, personalized, transparent, and privacy-protected healthcare decision-making using BDLT-integrated XAI.
  • The framework is assessed using several important metrics, including interpretability accuracy (92.85%), data security (98.97%), and privacy preservation (96.78%).
  • The research demonstrates an increase in AI-driven diagnoses by 97.12% using blockchain-verified data and a 94.33% trustworthiness rate in patient-physician interactions.
  • The framework seamlessly integrates with various healthcare apps, achieving 99.02% platform interoperability and up to 89.45% efficiency boost compared to state-of-the-art methods.
  • The scalability and effectiveness of the proposed framework are confirmed by simulations and real-world situations, emphasizing its potential to transform healthcare through the promotion of moral behavior.

Statistics:

  • Interpretability accuracy rate: 92.85%
  • Success rate in limiting unauthorized access: 98.97%
  • Compliance rate with data protection regulations: 96.78%
  • Increase in AI-driven diagnoses: 97.12%
  • Trustworthiness rate in patient-physician interactions: 94.33%
  • Efficiently integrates with various healthcare apps: 99.02%
  • Effciency boost compared to state-of-the-art methods: up to 89.45%

Sources:

  • Leveraging Blockchain-integrated Explainable Artificial Intelligence (Xai) for Ethical and Personalized Healthcare Decision-making: a Framework for Secure Data Sharing and Enhanced Patient Trust. The Journal of Supercomputing, 2025;81(15).
  • Abdullah ayub Khan, Bahria University, Dept. of Computer Sciences, Karachi Campus, Karachi 75260, Pakistan (additional information).
  • Mohamad Afendee Mohamed, Refka Ghodhbani, Abdulmajeed Alsufyani, and Nawal Alsufyani (authors).
  • NewsRx. New Personalized Medicine Study Findings Have Been Reported from Bahria University [Leveraging Blockchain-integrated Explainable Artificial Intelligence (Xai) for Ethical and Personalized Healthcare Decision-making: a Framework for Secure Data ...]. Information Technology Newsweekly. October 21, 2025; p 533.

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