Data Security in AI Healthcare Applications: Challenges and Innovative Methods

  1. Aleksandar Stankovic
  2. Marina Marjanovic

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Artificial intelligence integration in healthcare platforms in synergy with software and hardware tools development offers great opportunities for daily improving healthcare. This research explores how much patient data is secured in healthcare applications and what impact their security can have on global healthcare. Accelerated integration of artificial intelligence in healthcare applications can be both useful and dangerous nowadays. Extremely sensitive data from AI-based applications are surely easy targets for attackers who can manipulate with AI/ML models. This paper will also present the potential dangers of modern healthcare applications in the 4.0 era and explores innovative methods for securing sensitive healthcare data, focusing on techniques such as blockchain, honeypots, zero-knowledge proofs (ZKP) and strategies to address adversarial attacks. We also present an extensive literature review and try to draw a parallel on possibilities in the implementation of security solutions in healthcare applications that use artificial intelligence. Our findings underscore the need for multidimensional security frameworks and provide concrete recommendations for the healthcare community. Ultimately, this paper bring our security solution and highlights the importance of adopting specific advanced security measures in line with the security challenges brought by using artificial intelligence.

Article activity feed

  1. PREreview

    This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/18272597.

    Does the introduction explain the objective of the research presented in the preprint? Yes It is in line with the objectives that were indicated
    Are the methods well-suited for this research? Somewhat appropriate
    Are the conclusions supported by the data? Highly supported
    Are the data presentations, including visualizations, well-suited to represent the data? Somewhat appropriate and clear
    How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Very clearly
    Is the preprint likely to advance academic knowledge? Highly likely
    Would it benefit from language editing? No
    Would you recommend this preprint to others? Yes, it's of high quality
    Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes

    Competing interests

    The authors declare that they have no competing interests.

    Use of Artificial Intelligence (AI)

    The authors declare that they did not use generative AI to come up with new ideas for their review.

  2. Version published to 10.20944/preprints202507.0821.v1