Comparison of Artificial Intelligence and Traditional Methods in Preoperative Planning for Primary Total Hip Arthroplasty: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Although the application of artificial intelligence in orthopedics is becoming increasingly widespread, and initial progress has been made particularly in total hip arthroplasty (THA), its use in preoperative planning remains in the exploratory stage. Most existing studies are small‐scale observational studies with inconsistent results, making it difficult to establish a unified clinical consensus. Therefore, our study aims to explore the latest research developments and potential unique advantages of artificial intelligence in preoperative planning for THA. We conducted a comprehensive literature search in PubMed, Embase, Web of Science, and the Cochrane Library, covering all publications up to April 23, 2025. To evaluate study quality, we applied the revised Cochrane Risk of Bias tool for randomized controlled trials and the Newcastle‐Ottawa Scale (NOS) for non‐randomized studies. For the statistical analysis, odds ratios (OR) were used to assess categorical variables, while mean differences (MD) were calculated for continuous outcomes. Depending on the level of heterogeneity, a random‐effects model was adopted when substantial heterogeneity was detected ( I 2 > 50%); otherwise, a fixed‐effects model was applied. Through this process, a total of 518 studies were initially identified, of which 16 met the predefined inclusion criteria. The pooled analysis demonstrated that, in comparison to traditional methods, artificial intelligence achieved significantly superior outcomes in several key areas: acetabular‐side matching accuracy (OR = 0.24), femoral‐side matching accuracy (OR = 0.24), postoperative leg length discrepancy (MD = −1.02), operative time (MD = −12.18 min), intraoperative blood loss (MD = −50.82 mL), and postoperative Harris hip score (MD = 1.42). Notably, the overall methodological quality of the included studies was generally high. The final results of the study indicate that, compared to traditional preoperative planning, artificial intelligence in preoperative planning for THA can provide more precise surgical guidance, reduce surgical risks, and improve the overall success rate of the procedure. Trial Registration: PROSPERO registration number: CRD42024619714
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.002 |
| 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 | 0.000 |
| 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".