Integrating Artificial Intelligence and Virtual Reality in Orthopedic Surgery: A Comprehensive Review
Bibliographic record
Abstract
Artificial intelligence (AI) and virtual reality (VR) are being used in orthopedic surgery, with goals of enhancing surgical precision, trainee education, patient engagement, and personalized surgical strategies. AI-based predictive modeling, automated computer vision and image analytics, and robotic surgery are changing orthopedic preoperative planning and intraoperative decision-making, with the ultimate aim of improving postoperative outcomes through reduced variability in surgery. VR technologies are being used in orthopedic surgical simulations to provide safe environments for skill development in surgical trainees, helping them practice complex procedures before performing live surgeries. VR platforms are also being studied in-patient rehabilitation, focusing on interactive and gamified approaches that could enhance patients' adherence, recovery, and outcomes. Major pitfalls and challenges that need to be addressed include technical and logistical barriers, ethical concerns surrounding patient data privacy, and resistance to change among surgeons, trainees, and scientists. Improved infrastructure, standardized protocols, and further research to validate the long-term benefits will be imperative for the integration of AI and VR technologies into clinical and surgical workflows.
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 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".