Artificial intelligence in orthopedics: current applications, challenges, and future directions

Area of Science:

• Orthopedic research
• Medical artificial intelligence
• Clinical informatics

Background:

• Artificial intelligence (AI) research in orthopedics is advancing rapidly.
• A significant gap exists between AI technical development and its clinical application in orthopedics.
• This review examines current AI applications and implementation barriers in orthopedic practice.

Purpose of the Study:

• To summarize current AI applications in orthopedic practice.
• To highlight barriers hindering the clinical implementation of AI in orthopedics.
• To propose future directions for AI in orthopedic research and practice.

Main Methods:

• Narrative review of current literature on AI in orthopedics.
• Analysis of AI applications in perioperative risk prediction (machine learning), musculoskeletal imaging (deep learning), and workflow/decision support (large language models).
• Identification of barriers including algorithmic opacity, performance degradation, and workflow integration issues.

Main Results:

• AI applications like automated fracture detection and osteoarthritis grading are nearing clinical maturity.
• Key barriers to routine adoption include lack of transparency, performance variability, and poor workflow fit.
• Rigorous external validation and probability calibration are crucial for trustworthy AI.

Conclusions:

• The clinical impact of AI in orthopedics depends on life-cycle governance and proven net benefit.
• Prioritizing reliability and implementation science over retrospective performance benchmarks is essential.
• Future AI development may involve multimodal "digital twin" approaches for patient-specific care.