Scoping review: The use of augmented reality in clinical anatomical education and its assessment tools
Bibliographic record
Abstract
The purpose of this review was to identify the different augmented reality (AR) modalities used to teach anatomy to students, health professional trainees, and surgeons, and to examine the assessment tools used to evaluate the performance of various AR modalities. A scoping review of four databases was performed using variations of: (1) AR, (2) medical or anatomical teaching/education/training, and (3) anatomy or radiology or cadaver. Scientific articles were identified and screened for the inclusion and exclusion criteria as per Preferred Reporting Items for Systematic Reviews and Meta-Analyses with extension for scoping reviews guidelines. Virtual reality was an exclusion criterion. From this scoping review, data were extracted from a total of 54 articles and the following four AR modalities were identified: head-mounted display, projection, instrument and screen, and mobile device. The usability, feasibility, and acceptability of these AR modalities were evaluated using a variety of quantitative and qualitative assessment tools. Within more recent years of AR integration into anatomy education, the assessment of visuospatial ability, cognitive load, time on task, and increasing academic achievement outcomes are variables of interest, which continue to warrant more exploration. Sufficiently powered studies using validated assessment tools must be conducted to better understand the role of AR in anatomical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".