The study highlights the clinical potential of AI-powered 3D navigation systems in improving pedicle screw placement (PSP) accuracy while reducing radiation exposure and maintaining workflow efficiency. The X23D prototype achieved a breach rate of 21%, closely aligned with conventional techniques (24%), and recorded just one clinically significant breach (4%), showing its competitiveness with traditional navigation systems. Furthermore, X23D reduced average intraoperative radiation exposure to 33.26 mGy—significantly lower than both the adjusted fluoroscopy control group (49.47 mGy) and conventional fluoroscopy methods reported in literature (up to 113 mGy). From an operational perspective, X23D reduced the need for preoperative CT scans (which typically deliver 8.7–19.2 mGy), minimizing cumulative patient radiation and cost. Its real-time reconstruction speed (80 milliseconds per vertebra) and use of only four X-ray images make it highly efficient and patient-safe. Surgeons also reported improved user experience across all NASA-TLX dimensions, with particularly favorable scores in mental demand (2.4 vs. 4.0), temporal demand (1.6 vs. 3.2), and frustration (1.6 vs. 3.4). Importantly, the study demonstrates that AI-driven tools can integrate seamlessly into existing surgical environments with minimal disruption. The image acquisition occurred prior to incision, and no patient registration was required—addressing major limitations of current navigation systems. Given its positive surgeon feedback and technical reliability, X23D offers a scalable, low-barrier AI solution for improving spinal surgery precision. Broader clinical trials are warranted to validate its performance in real-time surgical settings and expand its use to other spinal regions.