From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment

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From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatmentMachine learning offers exciting potential for improved cancer detection, prognosis, and the identification of optimized therapies for patients. This review discusses advances and applications in machine learning models and techniques for the rich imaging and molecular data from the clinical oncology workflow, reviews the regulatory process for approving machine learning methods for cancer diagnostics, and outlines how to improve model design and evaluation to further adoption of machine learning in clinical oncology.Machine learning offers exciting potential for improved cancer detection, prognosis, and the identification of optimized therapies for patients. This review discusses advances and applications in machine learning models and techniques for the rich imaging and molecular data from the clinical oncology workflow, reviews the regulatory process for approving machine learning methods for cancer diagnostics, and outlines how to improve model design and evaluation to further adoption of machine learning in clinical oncology.Kyle Swanson, Eric Wu, Angela Zhang, Ash A. Alizadeh, James Zouhttps://www.cell.com/cell/fulltext/S0092-8674(23)00094-6?rss=yeshttp://www.cell.com/cell/inpress.rssCellCell RSS feed.Wireless News CampaignMarch 23, 2023

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