ARTIFICIAL INTELLIGENCE IN POSTOPERATIVE REHABILITATION PLANNING: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Artificial intelligence (AI) is increasingly being adopted in postoperative rehabilitation to enhance personalization, efficiency, and patient outcomes. Despite its growing use, evidence regarding the clinical effectiveness of AI-assisted rehabilitation protocols remains fragmented, with limited synthesis of outcome-based data across surgical populations. This systematic review was conducted to address this gap and evaluate the potential of AI in improving rehabilitation outcomes following surgery. Objective: This systematic review aims to assess the effectiveness and clinical outcomes of AI-assisted rehabilitation protocols compared to conventional rehabilitation methods in postoperative physical therapy. Methods: A systematic review was conducted following PRISMA guidelines. Four databases—PubMed, Scopus, Web of Science, and the Cochrane Library—were searched for studies published between January 2019 and March 2024. Eligible studies included randomized controlled trials and observational studies evaluating AI interventions in adult postoperative patients. Data extraction was performed using a standardized form, and study quality was assessed using the Cochrane Risk of Bias Tool and Newcastle-Ottawa Scale. Results: Eight studies met the inclusion criteria, encompassing a total of 1,021 patients undergoing various surgeries such as joint replacement, spinal, and abdominal procedures. AI interventions included predictive models, motion sensors, wearable devices, and virtual coaching platforms. Most studies reported significant improvements in functional recovery, pain reduction, and patient adherence in the AI-assisted groups (p < 0.05). However, heterogeneity in study designs and short follow-up durations limited data synthesis. Conclusion: AI-assisted rehabilitation shows promising benefits in enhancing postoperative outcomes compared to standard care. Although current findings support its clinical relevance, further large-scale, high-quality trials with long-term follow-up are necessary to establish reliability, cost-effectiveness, and implementation strategies.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".