Cybersecurity Challenges in Multimodal Medical Data: A Critical Review with a Focus on Diabetic Retinopathy Screening Systems
DOI:
https://doi.org/10.56294/dm20251248Keywords:
diabetic retinopathy screening, cybersecurity, multimodal medical imaging, DRSCI, metadata protection, quantum-resistant cryptographyAbstract
Introduction: this critical narrative review examined cybersecurity challenges in multimodal diabetic retinopathy (DR) screening systems, addressing the convergence of diverse data types within complex regulatory frameworks. With 537 million diabetics at risk globally and healthcare cyber incidents increasing by 45 % in 2023, the study investigated security vulnerabilities arising from integrating high-resolution imaging with clinical parameters.
Methods: the review employed an iterative search strategy across PubMed/MEDLINE, IEEE Xplore, Scopus, ACM Digital Library, and arXiv. From 487 initially identified publications, structured extraction and full-text review yielded 50 high-quality sources. The analysis synthesized findings through complexity theory, developing the novel Diabetic Retinopathy Security Complexity Index (DRSCI) to quantify multiplicative security challenges.
Results: the DRSCI revealed that 73 % of international collaborative screening programs exceeded manageable complexity thresholds (>1000), corresponding with vulnerability assessments showing 56 % of medical device vulnerabilities classified as critical or high-severity. The review identified critical gaps between theoretical security models and operational realities, particularly in multimodal data integration across jurisdictions. Current ISO 27799:2016 standards proved inadequate for addressing high-volume imaging data challenges.
Conclusions: the multimodal nature of modern DR screening created vulnerability surfaces transcending traditional security paradigms. The DRSCI framework transformed abstract risk assessments into actionable metrics, enabling evidence-based security investment decisions. Immediate priorities included developing quantum-resistant algorithms, implementing federated learning frameworks, and establishing comprehensive multimodal security standards before projected quantum computing threats materialize by 2030.
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