Harnessing Artificial Intelligence to Predict Dental Crowding in Modern Orthodontic Practice: A Comprehensive Review

Dental crowding is fundamental to orthodontic diagnosis and treatment planning, yet what constitutes “crowding” remains surprisingly heterogeneous. Space analysis quantifies tooth size–arch length discrepancy, irregularity indices characterize alignment displacement, and clinician‐assigned severity reflects broader diagnostic interpretation. This narrative review examines the evolution of crowding assessment from conventional space analysis and digital measurement to two‐dimensional artificial intelligence (AI), three‐dimensional point‐cloud learning, and clinician‐aware agentic AI. Digitalization improves efficiency and reproducibility but does not resolve fundamental dependencies on how crowding is defined, anatomically represented, and established as a reference standard. Two‐dimensional models enable prediction from readily available images but are constrained by projection and scale, whereas three‐dimensional approaches directly learn from dental surface representations and can capture local geometric relationships. In a companion study of 495 arches from 284 patients, PointNet++ achieved an R 2 of 0.718 and a mean absolute error (MAE) of 1.54 mm for prediction of crowding. In a separate retrospective demonstration, a bounded Gemini‐based Agent incorporating matched record‐derived verbal information reduced MAE from 1.951 to 1.437 mm, whereas mismatched information degraded performance. These findings also illustrate that the target construct—the specific clinical feature of crowding that a model is trained to predict—may differ across assessment methods. Together, these findings highlight a fundamental challenge: improving prediction does not resolve ambiguity in the target construct. Future crowding AI should therefore align the target construct, reference standard, and input representation; preserve provenance and uncertainty; integrate complementary clinical information transparently; and route uncertain cases for expert review.