Application of Mixed Reality for Ophthalmic Clinical Skills and Diagnosis: Prospective Study
Bibliographic record
Abstract
Background: Mixed reality has the potential to transform delivery of medical education. With tools such as HoloLens 2, educators can create immersive, interactive simulations that enable students to practice and engage with real-world scenarios in a controlled environment. Objective: We postulated that a hybrid ophthalmology curriculum incorporating EyelearnMR (a simulation application) would be noninferior to traditional teaching. We compared learning outcomes and obtained user feedback. Methods: This was a single-blind, cluster-randomized prospective study. Fourth-year medical students were organized into batches and then assigned to 2 groups: EyelearnMR and control arms. We used a quasi-randomized design with alternation allocation based on clinical grouping. The intervention group had an additional 2 hours of practice with the EyelearnMR devices. During the second week of their posting, a video assessment (5 scenarios with 17 multiple-choice questions) was conducted for both groups-mid-posting for the intervention group and at the end of the posting for the control group. The rationale for assessing the intervention group earlier, in addition to setting a higher bar for EyelearnMR, was to allow for the provision of outcomes showing noninferiority between both groups. In the event of noninferiority, we could demonstrate that EyelearnMR can replace some degree of traditional clinical teaching, even with a shorter total clinical exposure time. Students in the control group were allowed to experience the Eyelearn MR modules for 2 hours at the end of the posting. Both groups were asked to complete the User Experience Questionnaire. Results: This study was funded in February 2023, and recruitment took place from July 2023 to January 2024. A total of 54 students were recruited-24 (44.4%) in the control arm and 30 (55.6%) in the EyelearnMR arm. The EyelearnMR group performed significantly better than the control group (median scores of 16, IQR 15-17, and 15, IQR 14-15, respectively; P=.03; Mann-Whitney U test). A total of 100% (30/30) of the students in the EyelearnMR group scored full marks (3/3) for the technique portion, compared to 70.8% (17/24) of the students in the control group (P=.002). There was no statistically significant difference between the groups for the examination (P=.13) and pathology (P=.33) portions. This was despite the EyelearnMR group having a reduced clinical time of 7 days compared to 10 days in the control group. The User Experience Questionnaire showed positive evaluations for attractiveness (mean 1.413, SD 0.969), efficiency (mean 0.822, SD 1.068), dependability (mean 1.087, SD 0.801), stimulation (mean 1.577, SD 0.845), and novelty (mean 1.606, SD 0.967). Conclusions: EyelearnMR with traditional teaching was noninferior to traditional teaching alone. It provided a comparable experience and supported learning objectives equally. It is an effective supplementary teaching tool in ophthalmic education and may confer additional learning benefits beyond a traditional clinical posting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".