โก Quick Summary
This scoping review explores the application of artificial intelligence (AI) in postoperative orthopedic rehabilitation, highlighting its current use in risk prediction and rehabilitation monitoring. Despite advancements, the review reveals a significant gap in interventional AI strategies for enhancing patient recovery.
๐ Key Details
- ๐ Total articles reviewed: 38
- ๐งฉ Core AI technologies: Machine Learning (ML), Natural Language Processing (NLP), Expert Systems (ES)
- โ๏ธ Main application areas: Joint replacement, fracture repair, spinal surgery
- ๐ Focus of studies: Risk prediction, dynamic feedback, rehabilitation monitoring
๐ Key Takeaways
- ๐ AI is increasingly utilized in orthopedic postoperative care, primarily for predictive purposes.
- ๐ก Most studies emphasize short-term outcomes rather than long-term recovery.
- ๐ฉโโ๏ธ Limited evidence exists on AI’s role in nursing decision support and intervention adjustments.
- ๐ฅ Rehabilitation monitoring is a key area where AI can provide dynamic feedback.
- ๐ The review identifies a critical gap between predictive analytics and actionable rehabilitation strategies.
- ๐ฎ Future research should focus on longitudinal studies to enhance AI’s clinical applications.
๐ Background
The integration of artificial intelligence into healthcare has been transformative, particularly in orthopedic medicine. However, its role in postoperative rehabilitation remains underexplored. Rehabilitation is a continuous process, and understanding how AI can support this journey is crucial for improving patient outcomes.
๐๏ธ Study
This scoping review was conducted using the framework established by Arksey and O’Malley, involving a comprehensive literature search across multiple databases, including PubMed and CINAHL. The aim was to systematically map the current applications of AI in postoperative orthopedic rehabilitation and identify patterns and gaps in the existing evidence.
๐ Results
The review included a total of 38 articles that utilized three core AI technologies: Machine Learning (ML), Natural Language Processing (NLP), and Expert Systems (ES). These technologies were predominantly applied in scenarios involving joint replacements, fracture repairs, and spinal surgeries, focusing mainly on risk prediction and rehabilitation monitoring. However, most studies concentrated on short-term outcomes, with limited exploration of AI’s potential for long-term recovery and intervention adjustments.
๐ Impact and Implications
The findings of this review underscore the need for a paradigm shift in how AI is utilized in postoperative orthopedic rehabilitation. While current applications are promising, there is a pressing need for AI-enabled preventive and adaptive rehabilitation strategies that can translate data-driven insights into actionable clinical support. This could significantly enhance patient recovery trajectories and overall healthcare delivery.
๐ฎ Conclusion
This scoping review highlights the transformative potential of AI in postoperative orthopedic rehabilitation, yet it also reveals a critical gap in interventional applications. Future research should prioritize high-quality, longitudinal studies that explore the integration of AI into rehabilitation practices, aiming to bridge the divide between predictive analytics and effective clinical interventions. The journey towards a more intelligent rehabilitation process is just beginning!
๐ฌ Your comments
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Application of artificial intelligence in postoperative orthopedic rehabilitation: a scoping review.
Abstract
OBJECTIVES: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
Author: [‘Wang J’, ‘Bi H’, ‘Wang Y’, ‘Song Y’, ‘Xu H’, ‘Zhong S’, ‘He Q’, ‘Zhang Q’]
Journal: Front Digit Health
Citation: Wang J, et al. Application of artificial intelligence in postoperative orthopedic rehabilitation: a scoping review. Application of artificial intelligence in postoperative orthopedic rehabilitation: a scoping review. 2025; 7:1746552. doi: 10.3389/fdgth.2025.1746552