Aug 2026· Network Modeling Analysis in Health Informatics and Bioinformatics· Vol 15· 0 citations· 85 references
TL;DR
This systematic review synthesizes hybrid deep learning methods across mammography, ultrasound, MRI, histopathology/whole-slide imaging, and clinically oriented multimodal settings and provides practical reporting and evaluation recommendations to support more reliable and clinically ready systems.
Artificial intelligence shows substantial potential to enhance breast cancer imaging, but broader clinical translation requires robust external and prospective validation, improved calibration, assessment of generalizability and bias, and integration into clinical workflows.
I. Khan, Syed Taimoor Hussain Shah, Alexandra Tsipourakis et al.· Frontiers in Imaging· 0 citations
Background: Class imbalance remains a central methodological obstacle in machine learning (ML)-based breast cancer prediction, where the clinically critical minority class is systematically underrepresented relative to the majority class. A systematic review by Ghavidel and Pazos synthesized ML approaches to this probl...
Omega John Unogwu, Andrea Ngohide Abaagu, Ankar Tersoo Catherine· EDRAAK· 0 citations
Artificial intelligence algorithms exhibit robust diagnostic accuracy for csPCa detection, matching expert clinicians and offering significant potential to standardize prostate cancer care to establish long-term clinical utility.
Anjas Anhar Prastowo, M. Ali· World Journal of Advanced Re...· 0 citations
This paper presents a systematic review of deep learning approaches for predicting breast cancer recurrence from multimodal Magnetic Resonance Imaging and clinicopathological data, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.
Ahlam Ait Yahia, Ichrak Khoulqi, N. Idrissi· EPJ Web of Conferences· 0 citations
This systematic review synthesises peer-reviewed studies published between 2014 and 2025 that exclusively applied DL techniques to breast MRI for cancer classification, prediction, or segmentation and demonstrates that DL models consistently achieve high diagnostic performance and have the potential to enhance radiolog...
Qais Al-Azzam, W. Balachandran, Ziad Hunaiti· AI in Medicine· 0 citations
The review concludes that optimized, explainable, and adversarially robust ensemble frameworks, validated through multi-center clinical trials, represent the most promising direction for translating deep learning research into dependable and deployable breast cancer detection systems.
Abraham Temilade Olumide, Obe Olumide Olayinka, Akinwonmi Akintoba Emmanuel et al.· INTERNATIONAL JOURNAL OF MAT...· 0 citations
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