Wearable technology in the operating room: a systematic review
Bibliographic record
Abstract
Wearable technology is an emerging manifestation of consumer electronics that has the potential to revolutionise healthcare. The novel hands-free design and clinically relevant functionalities of various wearable devices hold significant promise for surgery, but the breadth and quality of evidence supporting clinical implementation in the operating room remains unclear. The objective of this article is to provide an objective overview of the available literature regarding the use of wearable technology in surgery, both in clinical and simulated experimental settings. A systematic review examining the use of wearable technology in surgery was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines using the MEDLINE and Web of Science databases from inception through 15 January 2016. Three authors independently screened the titles and abstracts of the retrieved articles and those that satisfied the defined inclusion criteria were selected for a full-text review. A total of 87 publications were included in this review. These articles predominantly described the use of Google Glass, GoPro or customised head-mounted displays (HMDs) in a wide range of intraoperative clinical settings. The included articles were categorised based on the highlighted areas of clinical impact, with the majority (56) discussing various applications for enhancing intraoperative safety and efficiency. Almost all articles cited technological limitations and privacy concerns as serious barriers to the implementation of wearable technology in the operating room. Evidence in the available literature regarding the use of wearable technology in the operating room shows promise, but high-quality clinical trials are needed to fully understand their clinical impact. Further, it will be essential to address existing technological limitations, develop healthcare-specific applications, and integrate privacy-protecting safeguards before it may be feasible for wearable devices to seamlessly integrate into the operative environment.
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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".