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

Lyuben Zyumbilski
Additional contact information
Lyuben Zyumbilski: University of National and World Economy, Sofia, Bulgaria

Innovative Information Technologies for Economy Digitalization (IITED), 2025, issue 1, 279-283

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.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.unwe.bg/doi/iited/2025/IITED.2025.36.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nwe:iitfed:y:2024:i:1:p:279-283

Access Statistics for this article

More articles in Innovative Information Technologies for Economy Digitalization (IITED) from University of National and World Economy, Sofia, Bulgaria Contact information at EDIRC.
Bibliographic data for series maintained by Vanya Lazarova ().