Dave Daksh, Akhunzada Adnan, Ivković Nikola, Gyawali Sujan, Cengiz Korhan, Ahmed Adeel, Al-Shamayleh Ahmad Sami
Department of Electrical Electronics, Birla Institute of Technology and Science, Birla Institute of Technology and Science, Pilani, India.
College of Computing IT, University of Doha for Science Technology, Doha, Qatar.
PeerJ Comput Sci. 2025 Feb 19;11:e2476. doi: 10.7717/peerj-cs.2476. eCollection 2025.
The integration of artificial intelligence into healthcare, particularly in mammography, holds immense potential for improving breast cancer diagnosis. Artificial intelligence (AI), with its ability to process vast amounts of data and detect intricate patterns, offers a solution to the limitations of traditional mammography, including missed diagnoses and false positives. This review focuses on the diagnostic accuracy of AI-assisted mammography, synthesizing findings from studies across different clinical settings and algorithms. The motivation for this research lies in addressing the need for enhanced diagnostic tools in breast cancer screening, where early detection can significantly impact patient outcomes. Although AI models have shown promising improvements in sensitivity and specificity, challenges such as algorithmic bias, interpretability, and the generalizability of models across diverse populations remain. The review concludes that while AI holds transformative potential in breast cancer screening, collaborative efforts between radiologists, AI developers, and policymakers are crucial for ensuring ethical, reliable, and inclusive integration into clinical practice.
将人工智能整合到医疗保健领域，尤其是在乳腺钼靶检查中，对于改善乳腺癌诊断具有巨大潜力。人工智能（AI）能够处理大量数据并检测复杂模式，为传统乳腺钼靶检查的局限性提供了解决方案，这些局限性包括漏诊和假阳性。本综述聚焦于人工智能辅助乳腺钼靶检查的诊断准确性，综合了来自不同临床环境和算法的研究结果。这项研究的动机在于满足乳腺癌筛查中对增强诊断工具的需求，早期检测对患者预后有重大影响。尽管人工智能模型在敏感性和特异性方面已显示出有前景的改善，但仍存在算法偏差、可解释性以及模型在不同人群中的通用性等挑战。综述得出结论，虽然人工智能在乳腺癌筛查中具有变革潜力，但放射科医生、人工智能开发者和政策制定者之间的合作对于确保其在临床实践中进行符合伦理、可靠且包容的整合至关重要。
