This talk describes the use of partially observable Markov decision processes (POMDPs) for personalizing cancer screening. POMDP models can be used to address several controversial open research questions in cancer screening, such as when to start and stop screening and how often to screen. We demonstrate the development and application of a POMDP-based personalized cancer screening policy using breast cancer as an example. In addition, we briefly describe how nonadherence to the screening recommendations, limited screening resources, and existence of chronic conditions could be addressed using the POMDP modeling framework. Finally, we describe successful POMDP applications in other cancers including personalizing colorectal and lung cancer screening.