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Review
. 2025 Aug 4;12(1):42.
doi: 10.1186/s40779-025-00633-z.

Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives

Jian Song #  1 , Guang-Chao Wang #  1 , Si-Cheng Wang #  2 , Chong-Ru He  1 , Ying-Ze Zhang  3 , Xiao Chen  4   5 , Jia-Can Su  6   7
Affiliations
Review

Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives

Jian Song et al. Mil Med Res. .

Abstract

Conventional diagnostic and therapeutic approaches in orthopedics are frequently time intensive and associated with elevated rates of diagnostic error, underscoring the urgent need for more efficient tools to improve the current situation. Recently, artificial intelligence (AI) has been increasingly integrated into orthopedic practice, providing data-driven approaches to support diagnostic and therapeutic processes. With the continuous advancement of AI technologies and their incorporation into routine orthopedic workflows, a comprehensive understanding of AI principles and their clinical applications has become increasingly essential. The review commences with a summary of the core concepts and historical evolution of AI, followed by an examination of machine learning and deep learning frameworks designed for orthopedic clinical and research applications. We then explore various AI-based applications in orthopedics, including image analysis, disease diagnosis, and treatment approaches such as surgical assistance, drug development, rehabilitation support, and personalized therapy. These applications are designed to help researchers and clinicians gain a deeper understanding of the current applications of AI in orthopedics. The review also highlights key challenges and limitations that affect the practical use of AI, such as data quality, model generalizability, and clinical validation. Finally, we discuss possible future directions for improving AI technologies and promoting their safe and effective integration into orthopedic care.

Keywords: Artificial intelligence (AI); Deep learning (DL); Diagnostic; Machine learning (ML); Orthopedics; Therapeutics.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
A brief history of AI development. GPT-4 generative pre-trained transformer 4
Fig. 2
Fig. 2
The relationship between AI, ML, and DL and commonly used algorithms as examples
Fig. 3
Fig. 3
Comprehensive applications of AI in orthopedics. DDH developmental dysplasia of the hip, 3D three-dimensional
See this image and copyright information in PMC

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