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

Autor:	Mehari M; Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA., Sibih Y; Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA., Dada A; Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA., Chang SM; Division of Neuro-Oncology, University of California San Francisco and Weill Institute for Neurosciences, San Francisco, California, USA., Wen PY; Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA., Molinaro AM; Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA., Chukwueke UN; Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA., Budhu JA; 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., Jackson S; Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, Pediatric Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., McFaline-Figueroa JR; Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA., Porter A; Division of Neuro-Oncology, Department of Neurology, Mayo Clinic, Phoenix, Arizona, USA., Hervey-Jumper SL; Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA.
Jazyk:	angličtina
Zdroj:	Neuro-oncology [Neuro Oncol] 2024 Nov 04; Vol. 26 (11), pp. 1951-1963.
DOI:	10.1093/neuonc/noae127
Abstrakt:	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. (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)
Databáze:	MEDLINE
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