AI/ML Trends in Oncology and the Rugged Path Towards the Clinic
In this installment of the NCI Imaging and Informatics Community Webinar, learn about the impending challenges and different approaches for detecting and mitigating bias when implementing artificial intelligence (AI) and machine learning (ML) algorithms within a clinical setting. Dr. Issam El Naqa of the Moffitt Cancer Center will give examples of how to apply these approaches in oncology applications and discuss their implications to pave the way for AI/ML in clinical practice.
AI and ML algorithms are currently transforming biomedical research, especially in the context of cancer research and clinical care. Despite the tremendous potentials in automating workflow, personalizing care, and reducing health disparity, AI/ML algorithm application in oncology and healthcare has been limited in scope with less than 5% of major healthcare providers implementing any form of AI/ML solutions, largely due to concerns about the deployment of AI/ML driven technologies within a clinical setting.
Dr. El Naqa is a renowned leader in the field of data science with formal training in electrical engineering, computer science, biology and medical physics. His research focuses on developing large-scale data mining methods to identify biomarkers of response to chemoradiotherapy, multimodality image-guided targeted and adaptive radiotherapy, and radiobiology-based treatment outcome modeling. He also serves as Chair of the Department of Machine Learning for the Moffitt Cancer Center.
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