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Artificial Intelligence (AI): A Potential Game Changer in Regenerative Orthopedics-A Scoping Review
Raju Vaishya1, Sakshi Dhall2, Abhishek Vaish1
1Department of Orthopaedics and Joint Replacement Surgery, Indraprastha Apollo Hospitals, Sarita Vihar, New Delhi, 110076 India.
Background And Aims:
Regenerative orthopedics involves approaches like stem cell therapy, platelet-rich plasma (PRP) therapy, the use of biological scaffold implants, tissue engineering, etc. We aim to present a scoping review of the role of artificial intelligence (AI) in different treatment approaches of regenerative orthopedics.
Methods:
Using the PRISMA guidelines, a search for articles for the last ten years (2013-2024) on PubMed was done, using several keywords. We have discussed the state-of-the-art, strengths/benefits, and limitations of the published research, and provide a useful resource for the way ahead in future for researchers working in this area.
Results:
Using the eligibility criteria out of 82 initially screened publications, we included 18 studies for this review. We noticed that the treatment responses to regenerative treatments depend on several factors; hence, to facilitate better comprehensive and patient-specific treatments, AI technology is very useful. Machine learning (ML) and deep learning (DL) are a few of the most frequently used AI techniques. They use a data-driven approach for training models to make human-like decisions. Data are fed to the ML/DL algorithm and the trained model makes classifications or predictions based on its learning.
Conclusion:
The area of regenerative orthopedics is highly sophisticated and significantly aids in providing cost-effective and non-invasive treatments to patients suffering from orthopedic ailments and injuries. Due to its promising future, the use of AI in regenerative orthopedics is an emerging and promising research field; however, its universal clinical applications are associated with some ethical considerations, which need addressing.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s43465-024-01189-1.

