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

Area of Science:

• Orthopedic Surgery
• Medical Imaging
• Artificial Intelligence

Background:

• Traditional orthopedic diagnostics and treatments are often time-consuming and prone to errors.
• There is a growing need for efficient tools to improve orthopedic care.
• Artificial intelligence (AI) is increasingly being integrated into orthopedic practice.

Purpose of the Study:

• To provide a comprehensive overview of AI principles and their clinical applications in orthopedics.
• To examine machine learning and deep learning frameworks relevant to orthopedic research and practice.
• To highlight current AI applications and future directions in the field.

Main Methods:

• Review of AI core concepts and historical evolution.
• Examination of machine learning and deep learning frameworks for orthopedics.
• Exploration of AI applications in orthopedic image analysis, diagnosis, and treatment.

Main Results:

• AI applications span image analysis, disease diagnosis, surgical assistance, drug development, rehabilitation, and personalized therapy.
• AI tools aim to deepen clinician and researcher understanding of orthopedic conditions.
• Key challenges include data quality, model generalizability, and clinical validation.

Conclusions:

• AI presents significant potential to improve efficiency and accuracy in orthopedic care.
• Addressing challenges in data and validation is crucial for effective AI integration.
• Future directions focus on advancing AI technologies for safe and effective clinical use in orthopedics.