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Cancer screening is a large, population-based intervention that would benefit from tools enabling individually-tailored decision making to decrease unintended consequences such as overdiagnosis. The heterogeneity of cancer screening participants advocates the need for more personalized approaches. Partially observable Markov decision processes (POMDPs) can be used to suggest optimal, individualized screening policies. However, determining an appropriate reward function can be challenging. Here, we propose the use of inverse reinforcement learning (IRL) to form rewards functions for lung and breast cancer screening POMDP models. Using data from the National Lung Screening Trial and our institution's breast screening registry, we developed two POMDP models with corresponding reward functions. Specifically, the maximum entropy (MaxEnt) IRL algorithm with an adaptive step size was used to learn rewards more efficiently; and combined with a multiplicative model to learn state-action pair rewards in the POMDP. The lung and breast cancer screening models were evaluated based on their ability to recommend appropriate screening decisions before the diagnosis of cancer. Results are comparable with experts' decisions. The lung POMDP demonstrated an improved performance in terms of recall and false positive rate in the second screening and post-screening stages. Precision (0.02-0.05) was comparable to experts' (0.02-0.06). The breast POMDP has excellent recall (0.97-1.00), matching the physicians and a satisfactory false positive rate (<0.03). The reward functions learned with the MaxEnt IRL algorithm, when combined with POMDP models in lung and breast cancer screening, demonstrate performance comparable to experts.