# Application of artificial intelligence in postoperative orthopedic rehabilitation: a scoping review

## TL;DR

This review maps how AI is used in post-surgery orthopedic rehab, finding it mostly helps predict outcomes rather than guide active treatment.

## Contribution

The novelty is a stage-based analysis of AI applications across the rehab process, highlighting gaps in actionable clinical support.

## Key findings

- AI is mainly used for risk prediction and monitoring in joint replacement, fracture, and spinal surgery rehab.
- Most studies focus on short-term outcomes, with limited evidence on long-term recovery or adaptive interventions.
- There is a gap between data-driven predictions and practical, AI-supported clinical decision-making in rehab.

## Abstract

Artificial intelligence (AI) has shown increasing promise is orthopedic medicine. However, its role in postoperative rehabilitation remains insufficiently synthesized, particularly when rehabilitation is viewed as a continuous and dynamic care process. This scoping review aims to systematically map current AI applications in postoperative orthopedic rehabilitation, indentify prevailing application patterns and evidence gaps, and clarify their clinical and nursing implications.

This scoping review was conducted following the methodological framework by Arksey and O’Malley. A comprehensive literature search was conducted in PubMed, CINAHL Complete, The Cochrane Library, Web of Science, Embase, Scopus, IEEE Xplore, SinoMed, China National Knowledge Infrastructure (CNKI), and the WanFang Database for studies published between March 2020 and March 2025. Data extraction and descriptive synthesis were performed on all included studies.

A total of 38 articles were included in this review, encompassing 3 core AI technologies, namely machine learning (ML), natural language processing (NLP), and expert systems (ES). These technologies were mainly applied in patients undergoing joint replacement, fracture repair, and spinal surgery, with the main application scenarios focusing on risk prediction, dynamic feedback, and rehabilitation monitoring. Notably, most studies focused on short-term predictive outcomes, while limited evidence addressed AI-supported intervention adjustment, nursing decision support, or long-term functional recovery.

This review highlights that, despite rapid technological progress, AI in postoperative orthopedic rehabilitation remains largely predictive rather than interventional. The novelty of this review lies in its stage-oriented synthesis of AI applications across the rehabilitation continuum, revealing a critical gap between data-driven prediction and clinically actionable rehabilitation support. Future research should prioritize high-quality, longitudinal studies and shift toward AI-enabled preventive and adaptive rehabilitation strategies to facilitate meaningful clinical translation.