Aug 2026· Naunyn-Schmiedeberg's Archives of Pharmacology· 0 citations· 35 references
Medicine
TL;DR
The objective of this study is to systematically evaluate the diagnostic performance, predictive capabilities, and clinical readiness of AI-based models in breast cancer imaging, with emphasis on methodological quality, validation strategies, and translational applicability.
Current evidence supports AI-assisted breast ultrasound's clinical potential, but further prospective multicenter studies with standardized methodologies and external validation are needed before routine clinical implementation.
W. A. Utami, Siti Wahyuni, Miska Zamharira et al.· Narra Review· 0 citations
This narrative review synthesizes the most recent evidence on AI applications in breast cancer screening and imaging, including landmark trial results, systematic reviews, and society recommendations, to demonstrate that AI-supported mammography screening can increase cancer detection rates and reduce interval cancer r...
Amrita Kumar, Gerald Lip· Indian Journal of Radiology...· 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
An engineering-oriented deployment framework is proposed, integrating modality-driven model selection, structured preprocessing pipelines, multi-level clinical validation, computational feasibility assessment, and explainability, together with a clinical deployment readiness model spanning validation maturity, data div...
Enoch Jacob Dodo, Amos Takai Yayock, Gregory Onwodi et al.· Journal of Science Research...· 0 citations
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
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