Innovations in Artificial Intelligence-Driven Breast Cancer Survival Prediction: A Narrative Review.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Cancer Informatics Pub Date : 2024-09-29 eCollection Date: 2024-01-01 DOI:10.1177/11769351241272389

Mehwish Mooghal, Saad Nasir, Aiman Arif, Wajiha Khan, Yasmin Abdul Rashid, Lubna M Vohra

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引用次数: 0

Abstract

This narrative review explores the burgeoning field of Artificial Intelligence (AI)-driven Breast Cancer (BC) survival prediction, emphasizing the transformative impact on patient care. From machine learning to deep neural networks, diverse models demonstrate the potential to refine prognosis accuracy and tailor treatment strategies. The literature underscores the need for clinician integration and addresses challenges of model generalizability and ethical considerations. Crucially, AI's promise extends to Low- and Middle-Income Countries (LMICs), presenting an opportunity to bridge healthcare disparities. Collaborative efforts in research, technology transfer, and education are essential to empower healthcare professionals in LMICs. As we navigate this frontier, AI emerges not only as a technological advancement but as a guiding light toward personalized, accessible BC care, marking a significant stride in the global fight against this formidable disease.

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人工智能驱动的乳腺癌生存预测创新：叙述性综述。

这篇叙述性综述探讨了人工智能（AI）驱动的乳腺癌（BC）生存预测这一新兴领域，强调其对患者护理的变革性影响。从机器学习到深度神经网络，各种模型都展示了提高预后准确性和定制治疗策略的潜力。文献强调了临床医生整合的必要性，并解决了模型通用性和伦理考虑方面的挑战。最重要的是，人工智能的前景已扩展到中低收入国家（LMIC），为缩小医疗差距提供了机会。研究、技术转让和教育方面的合作对于增强中低收入国家医疗保健专业人员的能力至关重要。在我们探索这一前沿领域的过程中，人工智能不仅是一项技术进步，也是实现个性化、可获得的 BC 护理的指路明灯，标志着全球在抗击这一可怕疾病的斗争中迈出了重要一步。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Informatics Medicine-Oncology

CiteScore

3.00

自引率

5.00%

发文量

审稿时长

8 weeks

期刊介绍： The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.