New Review on Artificial Intelligence and Anatomical Variation Research

A comprehensive narrative review examining the capabilities of artificial intelligence systems to detect and classify anatomical variations across clinical, educational, and methodological fields has been prepared. Covering the literature from January 2018 to September 2026, this study reveals that artificial intelligence concentrates heavily on dental and maxillofacial imaging, lumbosacral transitional vertebrae, and vascular configurations.

Definition and Scope of Anatomical Variations

Anatomical variation is defined as the general concept of non-pathological differences seen in the number, morphology, position, course, or relationships of structures. A normal variant is an accepted configuration that differs from the most common pattern but does not constitute a disease on its own.

The term atypical anatomy is used only in instances where an original study compares common configurations with less common or abnormal presentations. Rare anatomy is reserved for unusual configurations supported by evidence of prevalence.

Tasks of Artificial Intelligence in Anatomy

Artificial intelligence systems perform different tasks in anatomical examinations. These include detection, which finds the target; classification, which assigns a pre-determined category; and segmentation, which outlines a structure.

The recognition or segmentation of a general structure does not prove that a model has successfully identified a named variant. Systems, predominantly trained on common patterns, may reproduce the statistical norm.

Review Methods and Data Sources

A narrative design was chosen for the study because the evidence includes heterogeneous classification, segmentation, clinical planning, and educational studies. The objective is not to perform a systematic prevalence estimation, but rather to conduct a structured cross-system synthesis.

English reports published between January 2018 and September 2026 were evaluated by searching the PubMed/MEDLINE, Google Scholar, arXiv, and medRxiv databases.

Distribution and Synthesis of Existing Evidence

It is observed that the identified evidence is heavily concentrated in the field of dental and maxillofacial imaging. Direct studies also address lumbosacral transitional vertebrae and pulmonary, hepatic, or portal venous configurations.

Notable reports include the 2025 study by Kwak et al. on classifying lumbosacral transitional vertebrae, and the 2019 root classification research by Hiraiwa et al.