Scoping Review: The Use of Augmented Reality in Medical and Surgical Anatomical Education and Its Assessment Tools
Bibliographic record
Abstract
Introduction With the increasing accessibility to new technologies such as virtual reality and augmented reality (AR), a growing number of educators are exploring how to incorporate such advances to their field of study; anatomical education is no exception. Aim The purpose of this study was to identify the different AR modalities used to teach anatomy to students, medical/veterinarian trainees and surgeons via coursework, and/or procedural training. We also examined the qualitative and quantitative assessment tools used to evaluate the performance of various AR technologies in specific teaching settings. Methods A scoping review of the Web of Science, Pubmed, Embase and Medline was performed. Search terms were variations of 1) augmented reality, 2) medical or anatomical teaching/education/training, and 3) anatomy or radiology or cadaver. Abstracts, full articles, conference presentations, and guidelines published between January 2000–September 12, 2018 were identified and screened as per Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Virtual reality was an exclusion criterion. The number of participants, level of training, name of AR modality, aim of the study, setting of the study (anatomy course vs. procedural training), object that was “augmented”, body system studied, type of assessment tools, and relevant findings were extracted from accepted studies. Results Preliminary findings suggests that Microsoft Hololens and projection‐based modalities using Microsoft Kinect were the most prevalent modalities. The studies were mainly conducted in the context of an anatomy course, which primarily assessed usability, learner satisfaction and perceived benefits of AR through questionnaires using variations of the Likert scale. Certain studies also incorporated more objective findings such as pre‐ and post‐AR knowledge tests. However, the majority of those studies failed to use validated tests. Discussion/Conclusion The current literature seems supportive of the use of AR as an adjunctive teaching tool at the very least. However, the evidence is weak given the lack of studies with robust quantitative and qualitative methodology to objectively determine the influence of the integration of AR on anatomical education. Sufficiently powered studies using validated assessment tools must be conducted to better understand the role of AR in anatomical education. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0.026 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.028 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".