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AI/ML Trends in Oncology and the Rugged Path Towards the Clinic
(Issam El Naqa, PhD, Moffit Cancer Center)
Artificial intelligence (AI) and Machine learning (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, to name a few prospects, their 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. This can be attributed to multitude of concerning issues regarding the deployment of AI/ML driven technologies into the clinic. These concerns include but not limited to skepticism related to commercialization hype, under representative training data, inherent implementation bias, lack of robustness and absence of prediction transparency. In this work, we will discuss some of these impending challenges and highlight different approaches for detecting and mitigating such bias in implementing clinical AI/ML algorithms. We further show examples of applying these approaches in oncology applications from our work and others and discuss their implications to pave the way for AI/ML in clinical practice.