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Review Neuro-oncology. 2024 Nov 4;26(11):1951-1963. doi: 10.1093/neuonc/noae127 Q113.42025

Enhancing neuro-oncology care through equity-driven applications of artificial intelligence

通过公平驱动的人工智能应用增强神经肿瘤学护理 翻译改进

Mulki Mehari  1, Youssef Sibih  1, Abraham Dada  1, Susan M Chang  2, Patrick Y Wen  3, Annette M Molinaro  1, Ugonma N Chukwueke  3, Joshua A Budhu  4, Sadhana Jackson  5, J Ricardo McFaline-Figueroa  3, Alyx Porter  6, Shawn L Hervey-Jumper  1

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作者单位

  • 1 Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA.
  • 2 Division of Neuro-Oncology, University of California San Francisco and Weill Institute for Neurosciences, San Francisco, California, USA.
  • 3 Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.
  • 4 Department of Neurology, Memorial Sloan Kettering Cancer Center, Department of Neurology, Weill Cornell Medicine, Joan & Sanford I. Weill Medical College of Cornell University, New York, New York, USA.
  • 5 Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, Pediatric Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
  • 6 Division of Neuro-Oncology, Department of Neurology, Mayo Clinic, Phoenix, Arizona, USA.

    • DOI: 10.1093/neuonc/noae127 PMID: 39159285

    摘要 中英对照阅读

    The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient's demographics and social determinants of health. While current investigations in neuro-oncology have broadly utilized artificial intelligence (AI) to enrich tumor diagnosis and more accurately predict treatment response, postoperative complications, and survival, equity-driven applications of AI have been limited. However, AI applications to advance health equity in the broader medical field have the potential to serve as practical blueprints to address known disparities in neuro-oncologic care. In this consensus review, we will describe current applications of AI in neuro-oncology, postulate viable AI solutions for the most pressing inequities in neuro-oncology based on broader literature, propose a framework for the effective integration of equity into AI-based neuro-oncology research, and close with the limitations of AI.

    Keywords: artificial intelligence; health disparities; health equity; machine learning; neuro-oncology.

    Keywords：neuro-oncology care; artificial intelligence; health equity

    脑肿瘤患者的病程和临床结果不仅取决于肿瘤的分子和组织学特征，还取决于患者的人口统计学和健康的社会决定因素。虽然目前神经肿瘤学的研究已广泛利用人工智能（AI）来丰富肿瘤诊断，并更准确地预测治疗反应、术后并发症和生存率，但AI的公平驱动应用受到了限制。然而，在更广泛的医疗领域推进健康公平的人工智能应用有可能成为解决神经肿瘤护理中已知差异的实用蓝图。在这篇共识综述中，我们将描述人工智能在神经肿瘤学中的当前应用，根据更广泛的文献，为神经肿瘤学中最紧迫的不平等问题提出可行的人工智能解决方案，提出一个将公平有效整合到基于人工智能的神经肿瘤学研究中的框架，并接近人工智能的局限性。关键词：人工智能；健康差距；健康公平；机器学习；神经肿瘤学。©作者2024。牛津大学出版社代表神经肿瘤学会出版。保留所有权利。如需商业再利用，请联系reprints@oup.com重印和重印的翻译权。所有其他权限都可以通过我们网站文章页面上的权限链接通过我们的RightsLink服务获得——如需更多信息，请联系journals.permissions@oup.com.

    关键词：神经肿瘤护理; 人工智能; 健康公平性

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    期刊名：Neuro-oncology

    缩写：NEURO-ONCOLOGY

    ISSN：1522-8517

    e-ISSN：1523-5866

    IF/分区：13.4/Q1

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