Artificial intelligence in the context of the surgical treatment of scoliosis in adults, with an emphasis on applications, outcomes, and ethical implications: A systematic review
DOI:
https://doi.org/10.58814/01208845.542Keywords:
Scoliosis, Orthopedic Surgery, Artificial Intelligence, Postoperative Complications, Medical EthicsAbstract
Introduction: Artificial intelligence (AI) and machine learning (ML) are being increasingly implemented in the surgical treatment of scoliosis in adults in an effort to enhance precision, optimize outcomes, and support clinical decision-making. Despite significant progress, their use in the clinical setting raises ethical concerns regarding data governance, transparency, and algorithmic bias.
Objective: To systematically review the current evidence on the use of AI in the context of surgical treatment of scoliosis in adults, focusing on its clinical applications, reported outcomes, and associated ethical considerations.
Methodology: This systematic review was conducted in accordance with the PRISMA 2020 guidelines and registered in PROSPERO (CRD42024585554). A comprehensive search was performed in June 2024 across PubMed, ScienceDirect, Scopus, and Google Scholar. Studies addressing the use of AI or ML in the surgical treatment of scoliosis in adults (≥18 years) and reporting clinical applications, surgical outcomes, or ethical implications were included. Quality assessment was performed using the Newcastle-Ottawa Scale.
Results: A total of 304 records were retrieved from the searches. After removing duplicates and screening titles, abstracts, and full-text, 16 studies were included in the review. All studies were published between 2020 and 2024; 8 were observational studies, 1 was a systematic review, and 7 were literature reviews. The combined sample size of observational studies was 43 320 patients (141-39 254). Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Support Vector Machines (SVM) were predominant. Clinical applications encompassed predictive modeling of surgical outcomes, assessment of complication risks, and decision support for surgical planning. AI-enhanced systems showed potential to reduce complications and improve alignment outcomes. However, external validation was limited, and no study included prospective clinical trials. Ethical concerns such as transparency and data bias were acknowledged in only a minority of studies.
Conclusion: AI holds a considerable potential in scoliosis surgery for adults but it is still in early stages of clinical integration. Future research must focus on validation, explainability, and equitable implementation to fully realize its potential in spine surgery.
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References
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