Artificial Intelligence in Adrenal Diseases

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dc.authorid0000-0003-0022-5704
dc.authorid0000-0003-3590-2656
dc.contributor.authorSaygili, Emre Sedar
dc.contributor.authorKarakilic, Ersen
dc.date.accessioned2026-02-03T11:59:42Z
dc.date.available2026-02-03T11:59:42Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractAdrenal diseases present significant clinical challenges due to their complex pathophysiology and prevalence. Artificial intelligence (AI) advances have shown transformative potential in their diagnosis and management. Machine learning, deep learning, and radiomics have been explored for lesion detection, tumor characterization, and functional assessments. Artificial intelligence-assisted imaging enhances adrenal lesion identification and segmentation, particularly with computed tomography and magnetic resonance imaging, improving diagnostic accuracy and workflow efficiency. Radiomics aids in tumor differentiation and prognostic evaluations. Artificial intelligence models demonstrate significant potential in diagnosing adrenal lesions, including Cushing's syndrome, primary aldosteronism, pheochromocytomas, and adrenocortical carcinoma. Machine learning applications improve subtype classification, reduce invasive procedures, and refine risk stratification. Integrated AI models combining clinical, biochemical, and imaging data enhance treatment outcome predictions. Despite these advances, challenges remain, including data variability, model interpretability, ethical concerns, and regulatory constraints. The black box nature of AI complicates clinical integration, necessitating robust validation across diverse datasets. Identifying key parameters influencing model outcomes through various methods is crucial. Additionally, disparities in AI accessibility highlight the need for equitable implementation. While AI holds promise for adrenal disease management, further research is needed to enhance generalizability, address ethical concerns, and establish regulatory frameworks.
dc.identifier.doi10.5152/erp.2025.25520
dc.identifier.issn2822-6135
dc.identifier.issue3
dc.identifier.scopus2-s2.0-105010963478
dc.identifier.scopusqualityQ4
dc.identifier.trdizinid1343844
dc.identifier.urihttps://doi.org/10.5152/erp.2025.25520
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1343844
dc.identifier.urihttps://hdl.handle.net/20.500.12428/34381
dc.identifier.volume29
dc.identifier.wosWOS:001537958200014
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherAves
dc.relation.ispartofEndocrinology Research and Practice
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260130
dc.subjectAdrenal gland neoplasms
dc.subjectartificial intelligence
dc.subjectendocrine system diseases
dc.subjectmachine learning
dc.subjectradiomics
dc.titleArtificial Intelligence in Adrenal Diseases
dc.typeReview

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