  We present a real-time visualization system for Transcranial Magnetic
Stimulation (TMS), a non-invasive neuromodulation technique for treating
various brain disorders and mental health diseases. Our solution targets the
current challenges of slow and labor-intensive practices in treatment planning.
Integrating Deep Learning (DL), our system rapidly predicts electric field
(E-field) distributions in 0.2 seconds for precise and effective brain
stimulation. The core advancement lies in our tool's real-time neuronavigation
visualization capabilities, which support clinicians in making more informed
decisions quickly and effectively. We assess our system's performance through
three studies: First, a real-world use case scenario in a clinical setting,
providing concrete feedback on applicability and usability in a practical
environment. Second, a comparative analysis with another TMS tool focusing on
computational efficiency across various hardware platforms. Lastly, we
conducted an expert user study to measure usability and influence in optimizing
TMS treatment planning. The system is openly available for community use and
further development on GitHub: \url{https://github.com/lorifranke/SlicerTMS}.
