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  • Article Code : FIRAT-AKADEMI-10605-5779
  • Article Type : Derleme
  • Publication Number : 1A0501
  • Page Number : 129-147
  • Doi : 10.12739/NWSA.2025.20.4.1A0501
  • Abstract Reading : 120
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Issue Details

  • Year : 2025
  • Volume : 20
  • Issue : 4
  • Number of Articles Published : 6
  • Published Date : 1.10.2025

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Dergi Kapađý
Engineering Sciences

Serial Number : 1A
ISSN No. : 1308-7231
Release Interval (in a Year) : 4 Issues

Maral A. Mustafa1 , O. Ayhan Erdem2 , Esra Söđüt3

Keywords

ARTIFICIAL INTELLIGENCE IN BREAST CANCER DIAGNOSIS: CURRENT APPLICATIONS, CHALLENGES, AND THE ROLE OF EXPLAINABLE AI

Maral A. Mustafa1 , O. Ayhan Erdem2 , Esra Söđüt3

Breast cancer is the most commonly diagnosed cancer in women worldwide, and its prevalence is constantly increasing despite significant advancements in early diagnosis and personalised treatment. Nevertheless, current workflows for diagnostic interventions face challenges such as overdiagnosis in low-risk populations, increasing workloads for radiologists and pathologists, and inconsistent interpretation of imaging and pathological study findings. Artificial intelligence (AI) has proven to be an effective solution to these issues by enhancing image analysis, automating labour-intensive processes, and facilitating clinical decision-making. This paper presents a narrative review of recent AI implementations in breast cancer screening and diagnosis, including malignancy detection and classification, tumour segmentation, molecular subtype prediction, and recurrence or metastatic risk assessment. Data sources in both imaging and non-imaging domains are analysed, including mammography, ultrasound, magnetic resonance imaging (MRI), histopathology, clinical variables, and multimodal data integration. The reviewed articles also identify explainable artificial intelligence (XAI) methods, such as SHAP, Grad-CAM, and LIME, as being key to improving transparency, interpretability, and clinician confidence in AI-assisted systems. Overall, the current evidence suggests that AI-based tools have the potential to enhance diagnostic accuracy, reduce inter-observer variability, and facilitate personalised risk evaluation and treatment planning. However, there are still multiple obstacles to the widespread clinical implementation of AI in breast cancer care, such as the heterogeneity of datasets, a lack of external and prospective validation, interpretability issues, and constraints based on real-world application. Therefore, future studies must focus on creating more and better-quality data, establishing standard assessment guidelines and solid explainability models, and conducting future clinical trials, in order to enable the safe, productive and fair integration of AI into routine breast cancer care.

Keywords
Screening, Explainable AI, Malignancy Classification, Recurrence Rrediction, Image Segmentation,

Details
   

Authors

Maral A. Mustafa (1)

maralanwer@ntu.edu.iq | 0000-0002-0601-3457

O. Ayhan Erdem (2)

Gazi University
ayerdem@gazi.edu.tr | 0000-0001-7761-1078

Esra Söđüt (3) (Corresponding Author)
Gazi Üniversitesi Teknoloji Fakültesi Bilgisayar Mühendisliđi Bölümü
esrasogut@gazi.edu.tr | 0000-0002-0051-2271

Supporting Institution

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Project Number

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Thanks

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