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Ensuring patient privacy is a primary concern when considering the integration of artificial intelligence in healthcare. Advanced models have the capability to utilize and safeguard diverse patient datasets, ensuring secure data exchange and concealing personal health information. They can adapt to enhance blended learning, blockchain, natural language processing, cybersecurity, biometric authentication, and other techniques. However, ethical considerations, such as defining limits and eliminating biases, pose significant challenges. To address these concerns, increasing transparency and minimizing prejudice are crucial steps for the ethical integration of AI. In summary, the adoption of artificial intelligence in healthcare presents a significant opportunity to enhance patient privacy by implementing safeguard measures against unauthorized access to private information.

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Securing Patient Personal Information Using Multi-Dimensional Anonymization-Based Intelligent Technology Using Edge Nodes

  • 会议论文

  • 发表日期：

    2025年

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  • Abhinav Yadav,
  • Marushika Shukla,
  • Tiansheng Yang,
  • Rajkumar Singh Rathore,
  • Hrudaya Kumar Tripathy

摘要

Ensuring patient privacy is a primary concern when considering the integration of artificial intelligence in healthcare. Advanced models have the capability to utilize and safeguard diverse patient datasets, ensuring secure data exchange and concealing personal health information. They can adapt to enhance blended learning, blockchain, natural language processing, cybersecurity, biometric authentication, and other techniques. However, ethical considerations, such as defining limits and eliminating biases, pose significant challenges. To address these concerns, increasing transparency and minimizing prejudice are crucial steps for the ethical integration of AI. In summary, the adoption of artificial intelligence in healthcare presents a significant opportunity to enhance patient privacy by implementing safeguard measures against unauthorized access to private information.