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Artificial Intelligence in Medical Diagnostics: Algorithms, Data, and Challenges in Practical Implementation

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  • Lyuben Zyumbilski

    (University of National and World Economy, Sofia, Bulgaria)

Abstract

Artificial intelligence (AI) is reshaping medical diagnostics by transforming heterogeneous data imaging, clinical text, and physiological signals-into actionable predictions that support clinicians. This paper surveys core algorithmic approaches (supervised learning, deep learning, self supervised and foundation models), data management requirements, and the development lifecycle for diagnostic AI systems. We emphasize validation methodologies, calibration and generalization across sites, as well as workflow integration and human in the loop oversight. Ethical, legal, and organizational challenges are discussed with reference to GDPR, transparency, bias, and accountability. The paper distills engineering principles for building reliable, explainable, and safe AI tools that augment, rather than replace, clinical expertise.

Suggested Citation

  • Lyuben Zyumbilski, 2025. "Artificial Intelligence in Medical Diagnostics: Algorithms, Data, and Challenges in Practical Implementation," Innovative Information Technologies for Economy Digitalization (IITED), University of National and World Economy, Sofia, Bulgaria, issue 1, pages 279-283, October.
    • Handle: RePEc:nwe:iitfed:y:2024:i:1:p:279-283
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    File URL: https://www.unwe.bg/doi/iited/2025/IITED.2025.36.pdf
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    References listed on IDEAS

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