Generative Artificial Intelligence and Musculoskeletal Health Care

Area of Science:

• Artificial Intelligence
• Medical Technology
• Musculoskeletal Health

Background:

• Generative artificial intelligence (AI) models create novel outputs by learning from training data.
• Advanced models like diffusion and large language models show significant potential in healthcare applications.
• Generative AI is rapidly evolving due to increased computing power and data availability.

Purpose of the Study:

• To introduce fundamental concepts of generative AI.
• To present emerging clinical applications of generative AI in musculoskeletal healthcare.
• To highlight limitations hindering the clinical deployment of generative AI.

Main Methods:

• Review of generative AI concepts and technologies.
• Identification and discussion of current and developing applications in musculoskeletal healthcare.
• Analysis of limitations and challenges for clinical implementation.

Main Results:

• Generative AI can enhance medical images, generate multimedia content, automate administrative tasks, aid surgical planning, support treatment decisions, and personalize patient communication.
• Potential applications span various aspects of musculoskeletal care.
• Significant limitations including model "hallucinations", bias, ethical concerns, knowledge gaps, and lack of transparency exist.

Conclusions:

• Generative AI presents promising opportunities to advance musculoskeletal healthcare.
• Addressing identified limitations is crucial for safe and effective clinical integration.
• Further research and development are needed to overcome barriers to deployment.