Finally, we demonstrate that Tempo policies based on AI-based risk models outperform Tempo policies based on less accurate clinical risk models. Moreover, we show that the same Tempo policy can be easily adapted to a wide range of possible screening preferences, allowing clinicians to select their desired trade-off between early detection and screening costs without training new policies. Across all test sets, we find that the Tempo policy combined with an image-based artificial intelligence (AI) risk model is significantly more efficient than current regimens used in clinical practice in terms of simulated early detection per screen frequency. We trained our risk-based screening policies on a large screening mammography dataset from Massachusetts General Hospital (MGH USA) and validated this dataset in held-out patients from MGH and external datasets from Emory University (Emory USA), Karolinska Institute (Karolinska Sweden) and Chang Gung Memorial Hospital (CGMH Taiwan). Here, we introduce a novel reinforcement learning-based framework for personalized screening, Tempo, and demonstrate its efficacy in the context of breast cancer. Screening programs must balance the benefit of early detection with the cost of overscreening.
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