Optical Versus Virtual Microscope for Medical Education: A Systematic Review
Bibliographic record
Abstract
Many technological innovations have changed the traditional practice of medical education and clinical practice. Whole slide imaging (WSI) technology provided an unique way of viewing conventional glass slides in histology and pathology laboratories. The WSI technology digitalized glass slide images and made them readily accessible via the Internet using tablets or computers. Users utilized the pan-and-zoom function to view digital images of slides, also referred to as the virtual microscope (VM), simulating use of an optical microscope (OM). Several articles have reported various outcomes on the utility of VM in teaching laboratories. Recently, the Royal College of Physicians and Surgeons of Canada certification examinations for anatomical pathologists ha completely adopted VM for the national licensing examination. To better inform medical educators, there is an urgent need for more structured reviews to draw evidence-based conclusions on the effectiveness of VM and learner's perceptions, in comparison to OM. The current study provides a descriptive summary of published outcomes using the systematic review approach. In conclusion, medical students' performance was improved with adoption of VM into the curriculum and recognized as a preferred learning modality, compared to OM. On the contrary, resident learners' performance was comparable between using OM and VM, with OM being the favored slide-viewing modality.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".