Artificial Intelligence and Anatomical Variation: A Narrative Review of Detection, Classification, and Educational Applications.
4 affiliations
- 1Anatomy, Tanta University Hospital, Tanta, EGY.
- 2Medicine, St. George's University, St. George's, GRD.
- 3Research and Development, ANOVA Health, Riyadh, SAU.
- 4Basic Science, Lake Erie College of Osteopathic Medicine, Erie, USA.
Abstract
Anatomical variation encompasses non-pathological differences in the number, morphology, position, course, or relationships of structures and is not synonymous with rarity. Such variation affects imaging, surgery, procedural safety, dentistry, and anatomy education. This structured narrative review maps artificial intelligence (AI) applications that detect, classify, or segment variable anatomy across organ systems and distinguishes variant-specific evidence from general structure recognition. PubMed/MEDLINE, Google Scholar, publisher records, and preprint servers were searched iteratively on September 4, 2026, and updated on September 7, 2026, with citation chaining. The synthesis includes 14 principal empirical reports identified by this process: nine direct clinical imaging studies, three variation-relevant mapping studies, one direct multimodal preprint benchmark, and one contextual educational evaluation. Direct clinical evidence is concentrated in dental and cranio-maxillofacial imaging, with additional studies of lumbosacral transitional vertebrae and pulmonary, hepatic, or portal venous patterns. Performance is task-, dataset-, and prevalence-dependent; external validation, calibration, and transparent handling of correlated observations are uncommon. Segmentation performance does not establish named-variant classification, and evidence from anatomical proximity should not be transferred to untested variants. Educational outcome evidence is preliminary. Current AI may support narrowly specified tasks, but the identified literature does not establish a general, transferable understanding of human anatomical diversity. Progress requires explicit taxonomies, patient-level partitions, representative class distributions, clinically defensible reference standards, external validation, and dedicated educational evaluation.
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BibTeX
@article{mahgoub2026artificial,
title = {{Artificial Intelligence and Anatomical Variation: A Narrative Review of Detection, Classification, and Educational Applications.}},
author = {Ahmed Mahgoub and Anaida Singh and Ayman Mahgoub and Deepak Sharma},
journal = {Cureus},
year = {2026},
month = sep,
doi = {10.7759/cureus.116649},
url = {https://europepmc.org/article/MED/42910330},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial Intelligence and Anatomical Variation: A Narrative Review of Detection, Classification, and Educational Applications.
AU - Ahmed Mahgoub
AU - Anaida Singh
AU - Ayman Mahgoub
AU - Deepak Sharma
PY - 2026
DA - 2026/09/21
JO - Cureus
DO - 10.7759/cureus.116649
UR - https://europepmc.org/article/MED/42910330
AB - Anatomical variation encompasses non-pathological differences in the number, morphology, position, course, or relationships of structures and is not synonymous with rarity. Such variation affects imaging, surgery, procedural safety, dentistry, and anatomy education. This structured narrative review maps artificial intelligence (AI) applications that detect, classify, or segment variable anatomy across organ systems and distinguishes variant-specific evidence from general structure recognition. PubMed/MEDLINE, Google Scholar, publisher records, and preprint servers were searched iteratively on September 4, 2026, and updated on September 7, 2026, with citation chaining. The synthesis includes 14 principal empirical reports identified by this process: nine direct clinical imaging studies, three variation-relevant mapping studies, one direct multimodal preprint benchmark, and one contextual educational evaluation. Direct clinical evidence is concentrated in dental and cranio-maxillofacial imaging, with additional studies of lumbosacral transitional vertebrae and pulmonary, hepatic, or portal venous patterns. Performance is task-, dataset-, and prevalence-dependent; external validation, calibration, and transparent handling of correlated observations are uncommon. Segmentation performance does not establish named-variant classification, and evidence from anatomical proximity should not be transferred to untested variants. Educational outcome evidence is preliminary. Current AI may support narrowly specified tasks, but the identified literature does not establish a general, transferable understanding of human anatomical diversity. Progress requires explicit taxonomies, patient-level partitions, representative class distributions, clinically defensible reference standards, external validation, and dedicated educational evaluation.
SN - 2168-8184
ER -