AI for Medical Image Security: A Comprehensive Review of Techniques and Challenges

Ensuring the security of sensitive medical data, including patient records and medical images, is paramount in the healthcare sector due to the risks of unauthorized access and data breaches. As healthcare information is increasingly transmitted through unsecured channels, maintaining its confidentiality, integrity, and authenticity is essential. This review examines AI-driven security techniques such as encryption, anomaly detection, and privacy-preserving algorithms, which play a crucial role in protecting medical data. By enhancing regulatory compliance and fostering trust in digital healthcare systems, these methods contribute significantly to data security. Additionally, this paper explores recent advancements in AI-based medical image protection and highlights key challenges and future research directions in the field of medical data security.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-06
DOI
https://doi.org/10.5281/zenodo.17543986
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
0.00
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article

AI for Medical Image Security: A Comprehensive Review of Techniques and Challenges

Zenodo (CERN European Organization for Nuclear Research)
COVID-19 diagnosis using AI
article

AI for Medical Image Security: A Comprehensive Review of Techniques and Challenges

article en

Abstract

Ensuring the security of sensitive medical data, including patient records and medical images, is paramount in the healthcare sector due to the risks of unauthorized access and data breaches. As healthcare information is increasingly transmitted through unsecured channels, maintaining its confidentiality, integrity, and authenticity is essential. This review examines AI-driven security techniques such as encryption, anomaly detection, and privacy-preserving algorithms, which play a crucial role in protecting medical data. By enhancing regulatory compliance and fostering trust in digital healthcare systems, these methods contribute significantly to data security. Additionally, this paper explores recent advancements in AI-based medical image protection and highlights key challenges and future research directions in the field of medical data security.

Zenodo (CERN European Organization for Nuclear Research)
Systems Technology (United States) (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 35%
COVID-19 diagnosis using AI
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