Inattentional Blindness Increased with Augmented Reality Surgical Navigation
Bibliographic record
Abstract
BACKGROUND: Augmented reality (AR) surgical navigation systems, designed to increase accuracy and efficiency, have been shown to negatively impact on attention. We wished to assess the effect "head-up" AR displays have on attention, efficiency, and accuracy, while performing a surgical task, compared with the same information being presented on a submonitor (SM). METHODS: Fifty experienced otolaryngology surgeons (n = 42) and senior otolaryngology trainees (n = 8) performed an endoscopic surgical navigation exercise on a predissected cadaveric model. Computed tomography-generated anatomic contours were fused with the endoscopic image to provide an AR view. Subjects were randomized to perform the task with a standard endoscopic monitor with the AR navigation displayed on an SM or with AR as a single display. Accuracy, task completion time, and the recognition of unexpected findings (a foreign body and a critical complication) were recorded. RESULTS: Recognition of the foreign body was significantly better in the SM group (15/25 [60%]) compared with the AR alone group (8/25 [32%]; p = 0.02). There was no significant difference in task completion time (p = 0.83) or accuracy (p = 0.78) between the two groups. CONCLUSION: Providing identical surgical navigation on a SM, rather than on a single head-up display, reduced the level of inattentional blindness as measured by detection of unexpected findings. These gains were achieved without any measurable impact on efficiency or accuracy. AR displays may distract the user and we caution injudicious adoption of this technology for medical procedures.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".