From “Who Knows Anatomy Best?” to “What Does ‘Best’ Mean?”: Strengthening Validation Standards for Large Language Models in Anatomy Education

{"Large":[0],"language":[1],"models":[2],"(LLMs)":[3],"are":[4,45,151],"increasingly":[5],"benchmarked":[6],"against":[7],"professional":[8],"examination":[9],"questions":[10,29,150],"and":[11,36,64,73,89,99,107,115,160,167,181],"interpreted":[12],"as":[13],"indicators":[14],"of":[15,69,96,110,158],"educational":[16,65,182],"competence.":[17],"A":[18],"recent":[19,104],"study":[20],"comparing":[21],"four":[22],"LLMs":[23],"on":[24,103],"publicly":[25,85],"available":[26],"anatomy":[27,146],"multiple-choice":[28],"reported":[30],"near-ceiling":[31],"performance":[32,49],"for":[33,145],"one":[34],"model":[35,97,153],"statistically":[37],"significant":[38],"differences":[39],"among":[40],"competitors.":[41],"While":[42],"such":[43,136],"comparisons":[44,128],"informative,":[46],"interpreting":[47],"\\"best\\"":[48],"requires":[50],"careful":[51],"validation":[52,130],"framing.":[53],"This":[54],"commentary":[55],"highlights":[56],"three":[57],"methodological":[58],"domains":[59],"that":[60,121,135],"critically":[61],"influence":[62],"inference":[63],"translation:":[66],"(1)":[67],"alignment":[68],"the":[70,148,156],"inferential":[71],"unit":[72],"uncertainty":[74],"reporting":[75,95],"in":[76,113],"paired":[77],"item-based":[78],"designs;":[79],"(2)":[80],"contamination":[81],"risk":[82],"inherent":[83],"to":[84,165],"accessible":[86],"exam":[87],"banks;":[88],"(3)":[90],"reproducibility":[91],"requirements,":[92],"including":[93],"transparent":[94],"configuration":[98],"evaluation":[100],"conditions.":[101],"Drawing":[102],"high-impact":[105],"guidance":[106],"empirical":[108],"evaluations":[109],"artificial":[111],"intelligence":[112],"health":[114],"education,":[116,147],"we":[117],"propose":[118],"practical":[119],"refinements":[120,171],"would":[122],"shift":[123],"anatomy-LLM":[124],"research":[125],"from":[126],"leaderboard-style":[127],"toward":[129],"science.":[131],"We":[132],"further":[133],"argue":[134],"refinement":[137],"is":[138],"a":[139],"means":[140],"rather":[141],"than":[142],"an":[143],"end:":[144],"decisive":[149],"whether":[152,161],"errors":[154],"resemble":[155],"misconceptions":[157],"students":[159],"text-based":[162],"accuracy":[163],"transfers":[164],"three-dimensional":[166],"spatial":[168],"tasks.":[169],"These":[170],"do":[172],"not":[173],"diminish":[174],"current":[175],"findings":[176],"but":[177],"enhance":[178],"their":[179],"interpretability":[180],"credibility.":[183]}

Authors

Institutions

Publication Details

Journal
Clinical Anatomy
Published
2026-09-15
DOI
https://doi.org/10.1002/ca.70208
Primary Topic
Anatomy and Medical Technology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

From “Who Knows Anatomy Best?” to “What Does ‘Best’ Mean?”: Strengthening Validation Standards for Large Language Models in Anatomy Education

Carlos Fernando Mourão, Luiz Eduardo Rodrigues Juliasse, Rodrigo dos Santos Pereira
Clinical Anatomy
Anatomy and Medical Technology
article

From “Who Knows Anatomy Best?” to “What Does ‘Best’ Mean?”: Strengthening Validation Standards for Large Language Models in Anatomy Education

Carlos Fernando Mourão, Luiz Eduardo Rodrigues Juliasse, Rodrigo dos Santos Pereira
article en

Abstract

Large language models (LLMs) are increasingly benchmarked against professional examination questions and interpreted as indicators of educational competence. A recent study comparing four LLMs on publicly available anatomy multiple-choice questions reported near-ceiling performance for one model and statistically significant differences among competitors. While such comparisons are informative, interpreting "best" performance requires careful validation framing. This commentary highlights three methodological domains that critically influence inference and educational translation: (1) alignment of the inferential unit and uncertainty reporting in paired item-based designs; (2) contamination risk inherent to publicly accessible exam banks; and (3) reproducibility requirements, including transparent reporting of model configuration and evaluation conditions. Drawing on recent high-impact guidance and empirical evaluations of artificial intelligence in health and education, we propose practical refinements that would shift anatomy-LLM research from leaderboard-style comparisons toward validation science. We further argue that such refinement is a means rather than an end: for anatomy education, the decisive questions are whether model errors resemble the misconceptions of students and whether text-based accuracy transfers to three-dimensional and spatial tasks. These refinements do not diminish current findings but enhance their interpretability and educational credibility.

Clinical Anatomy
Tufts University (US), Universidade Federal do Rio de Janeiro (BR)
Quality Education
Openalex Percentile: Top 21%
Anatomy and Medical Technology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.