ReviewCureus2026
Terminologia Anatomica Compliance in Generative Artificial Intelligence Outputs: A Structured Narrative Review and Terminology-Specific Verification Checklist.
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
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence (GenAI) can produce fluent anatomical explanations and persuasive illustrations, but fluency and visual realism do not establish compliance with Terminologia Anatomica, second edition (TA2), or anatomical correctness. Unlike broader anatomy-education governance frameworks, this evidence-informed narrative review isolates terminology compliance as a distinct validation problem. It examines six connected domains: baseline human and editorial use of standardized terminology, text-based GenAI performance, image-generation fidelity, citation reliability, multilingual terminology, and publisher oversight. Direct evidence is heterogeneous but increasingly specific. Recent studies have used dedicated terminology measures or TA2 as a reference standard for text outputs, while image studies consistently identify omissions, invented structures, incorrect relationships, and mislabeled anatomy. One anatomy-specific citation study also found substantial reference hallucination and limited PubMed identifier accuracy. These risks occur against a pre-existing background of incomplete TA adoption among anatomists and journal editors, so GenAI may reproduce or amplify inconsistencies already present in its source environment. Evidence concerning non-English TA2 output remains particularly limited. A four-step terminology-specific checklist is therefore proposed: classify the output and its risk; verify every anatomical term against an authoritative terminology source; verify scientific content, structural fidelity, and source traceability; and document accountable human review and disclose AI assistance. Verification should be tiered by consequence: low for internal brainstorming, moderate for teaching materials, and high for summative assessment, publication, and clinical or procedural use. The checklist is intentionally practical rather than a validated scoring instrument. Current evidence does not support treating any general-purpose GenAI system as reliably TA2-compliant without human verification.
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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.