Machine Learning and Artificial Intelligence in Metallic Orthopedic Implant Development: A Narrative Review

Area of Science:

• Biomaterials Science
• Orthopedic Engineering
• Artificial Intelligence in Medicine

Background:

• Metallic orthopedic implants present persistent clinical challenges resistant to conventional development.
• Machine learning (ML) and artificial intelligence (AI) offer a paradigm for navigating complex implant design spaces.
• Existing literature is fragmented, lacking a comprehensive synthesis of AI/ML across the implant development pipeline.

Purpose of the Study:

• To synthesize the application of AI/ML across the orthopedic implant development pipeline.
• To identify advancements and challenges in using AI/ML for implant design and performance prediction.
• To provide a roadmap for future research and clinical translation.

Main Methods:

• A systematic database search (PubMed/MEDLINE, Embase, Cochrane) was conducted.
• 33 primary studies were analyzed across five domains: alloy discovery, additive manufacturing, structure design, surface engineering, and performance prediction.
• Hand-searching of reference lists supplemented the database search.

Main Results:

• ML approaches significantly accelerate property prediction and design space exploration compared to traditional methods.
• Representative performance includes accurate modulus predictions and high R² values for property prediction (up to 0.9991).
• Key barriers to translation include data limitations, lack of external validation, interpretability issues, and absence of regulatory frameworks.

Conclusions:

• Standardized, open databases linking material parameters to clinical outcomes are crucial.
• Prospective clinical validation studies are necessary to establish AI-assisted device efficacy.
• Coordinated engagement between researchers, industry, and regulatory bodies is essential for successful translation.