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In this article we describe the implementation of Artificial Intelligencemodels in track reconstruction software for the CLAS12 detector at JeffersonLab. The Artificial Intelligence based approach resulted in improved trackreconstruction efficiency in high luminosity experimental conditions. The trackreconstruction efficiency increased by $10-12\%$ for single particle, andstatistics in multi-particle physics reactions increased by $15\%-35\%$depending on the number of particles in the reaction. The implementation ofartificial intelligence in the workflow also resulted in a speedup of thetracking by $35\%$.

CLAS12 Track Reconstruction with Artificial Intelligence