Abstract

AI-based medical imaging systems have achieved remarkable gains in diagnostic accuracy, yet trust, explainability and ethical issues remain central to clinical adoption. This review systematically examines 142 studies published between 2017 and 2024. Explainability methods are classified as model-agnostic or model-specific, and evaluation metrics and clinical validation practices are discussed. Gaps concerning data bias, accountability and patient privacy are identified, and a roadmap for future research is presented.

Keywords:explainable artificial intelligencemedical imagingethicstrustworthinesssystematic review

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Ethics Approval
Bu çalışma etik kurul onayı gerektirmemektedir.
Conflict of Interest
Yazarlar herhangi bir çıkar çatışması olmadığını beyan eder.
Funding
Bu çalışma Selçuk Üniversitesi BAP Koordinatörlüğü tarafından desteklenmiştir (Proje No: 24009).

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How to Cite

Karaca, E., & Aydoğdu, Z. (2025). Artificial Intelligence in Medical Imaging: A Review of Explainability, Trustworthiness and Ethical Dimensions. International Journal of Science and Technology Research, 7(2), 35–45. https://doi.org/10.99999/ubtad.2025.10
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CC BY 4.0

© 2025 Elif Karaca, Zeynep Aydoğdu. This article is distributed under the terms of the CC BY 4.0 license, which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. License text