ReviewCureus2026
Artificial Intelligence and Anatomical Variation: A Narrative Review of Detection, Classification, and Educational Applications.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.