  This paper presents the results of charged particle track reconstruction in
CLAS12 using artificial intelligence. In our approach, we use machine learning
algorithms to reconstruct tracks, including their momentum and direction, with
high accuracy from raw hits of the CLAS12 drift chambers. The reconstruction is
performed in real-time, with the rate of data acquisition, and allows for the
identification of event topologies in real-time. This approach revolutionizes
the Nuclear Physics experiments' data processing, allowing us to identify and
categorize the experimental data on the fly, and will lead to a significant
reduction in experiment data processing. It can also be used in streaming
readout applications leading to more efficient data acquisition and
post-processing.
