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PurposeThis paper presents a method to search for the worst‐case configuration leading to the highest RF exposure for a multiconfiguration implantable fixation system under MRI. MethodsA two‐step method combining an artificial neural network and a genetic algorithm is developed to achieve this purpose. In the first step, the level of RF exposure in terms of peak 1‐g and/or 10‐g averaged specific absorption rate (SAR_1g/10g), related to the multiconfiguration system, is predicted using an artificial neural network. A genetic algorithm is then used to search for the worst‐case configuration of this multidimensional nonlinear problem within both the enumerated discrete sample space and generalized continuous sample space. As an example, a generic plate system with a total of 576 configurations is used for both 1.5T and 3T MRI systems. ResultsThe presented method can effectively identify the worst‐case configuration and accurately predict the SAR_1g/10gwith no more than 20% of the samples in the studied discrete sample space, and can even predict the worst case in the generalized continuous sample space. The worst‐case prediction error in the generalized continuous sample space is less than 1.6% for SAR_1gand less than 1.3% for SAR_10gcompared with the simulation results. ConclusionThe combination of an artificial neural network with genetic algorithm is a robust technique to determine the worst‐case RF exposure level for a multiconfiguration system, and only needs a small amount of training data from the entire system.