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Generative Artificial Intelligence (GenAI) offers transformative healthcare applications but poses cybersecurity risks like data privacy issues and bias. This review explores GenAI

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Area of Science:

  • Healthcare AI
  • Cybersecurity in AI
  • Generative Artificial Intelligence

Background:

  • Generative Artificial Intelligence (GenAI) represents a significant technological advancement, capable of producing novel content.
  • Its rapid integration across industries highlights its potential to revolutionize various sectors, including healthcare.
  • Understanding GenAI's capabilities and associated risks is crucial for its effective adoption.

Purpose of the Study:

  • To explore the diverse applications of GenAI in healthcare, encompassing clinical, operational, and research domains.
  • To identify and analyze the inherent cybersecurity risks associated with GenAI technologies.
  • To propose strategies for mitigating these risks and leveraging GenAI for enhanced cybersecurity.

Main Methods:

  • Literature review of Generative Artificial Intelligence applications in healthcare.
  • Analysis of cybersecurity vulnerabilities specific to GenAI, including data privacy, poisoning, bias, and hallucinations.
  • Exploration of risk mitigation frameworks and the dual use of GenAI in cybersecurity.

Main Results:

  • GenAI presents numerous opportunities in healthcare, from diagnostics to drug discovery.
  • Significant cybersecurity risks are identified, including data breaches, model manipulation, and the generation of inaccurate information (hallucinations).
  • Effective risk management strategies are essential for secure GenAI implementation.

Conclusions:

  • GenAI is a pivotal technology with the potential to reshape healthcare delivery and research.
  • Addressing cybersecurity challenges proactively is imperative to harness GenAI's full benefits safely.
  • Further research into secure GenAI development and deployment is warranted.