Traditionally, breast cancer detection has relied on radiological imaging techniques and expert interpretation, which, although effective, remain vulnerable to diagnostic variability, delayed reporting, and human error. In recent years, artificial intelligence has emerged as a promising paradigm in medical imaging by introducing automated feature extraction, deep learning–based classification, and predictive analytics into diagnostic workflows. However, despite these advantages, AI-based detection systems are not universally optimal and face inherent limitations related to dataset bias, model interpretability, computational complexity, and regulatory compliance. As data volume and population diversity increase, these challenges can impact system reliability and clinical deployment. This study presents a comprehensive review of AI applications in enhanced breast cancer detection under the proposed OncoVision AI framework, examining their role in improving diagnostic accuracy and early-stage identification. The analysis identifies that while AI significantly enhances sensitivity, efficiency, and decision support in radiology, its effectiveness depends on robust training datasets, explainable model design, and integration with clinical expertise and healthcare infrastructure.

Artificial intelligence offers a practical and transformative approach to addressing many of the diagnostic challenges associated with modern breast cancer detection. Traditional screening systems, although clinically established and widely adopted, remain vulnerable to interpretative variability, delayed reporting, and the possibility of missed early-stage abnormalities. The analysis presented in this study demonstrates that AI-driven architectures, particularly deep learning models with automated feature extraction and predictive capabilities, significantly enhance diagnostic accuracy, sensitivity, and clinical decision support in medical imaging environments. The findings emphasize a clear distinction between conventional diagnostic workflows and AI-assisted frameworks. In large-scale screening programs and high-volume radiology departments, AI reduces reliance solely on manual interpretation and supports clinicians by identifying subtle imaging patterns that may otherwise be overlooked. However, the study also highlights that AI adoption is not without limitations. Computational demands, dataset bias, ethical considerations, and regulatory compliance remain critical factors influencing real-world implementation and clinical reliability. Importantly, the effectiveness of AI-based breast cancer detection systems depends heavily on model design, training data diversity, validation protocols, and explainability mechanisms. While AI strengthens early detection and improves workflow efficiency in environments prioritizing precision and scalability, it may face challenges in resource-constrained settings or scenarios requiring complete transparency in decision-making. Therefore, AI should be considered a complementary clinical decision-support tool rather than a full replacement for radiological expertise. Overall, this review confirms that AI extends far beyond traditional computer-aided detection methods and represents a powerful advancement in precision oncology. When thoughtfully integrated with healthcare infrastructure, electronic health records, and multidisciplinary clinical practice, frameworks such as OncoVision AI have the potential to establish a resilient and intelligent foundation for next-generation breast cancer diagnostic systems.

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