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Published on: July 11, 2025
Artificial Intelligence in Value-Based Health Care.
Romil Shah1, Kevin J Bozic2, Prakash Jayakumar2
1Department of Orthopedic Surgery, Cedars Sinai Medical Center, Los Angeles, CA, USA.
Summary
Artificial intelligence (AI) can advance orthopedic surgery by enhancing patient agency, automating tasks, and augmenting surgical capabilities. Challenges include professional resistance, data concerns, and financial investment for successful AI integration.
Area of Science:
- Orthopedic Surgery
- Health Informatics
- Medical Technology
Background:
- Value-based healthcare models are transforming orthopedic surgery.
- Artificial intelligence (AI) offers novel mechanisms to enhance healthcare delivery.
- Optimizing patient outcomes and operational efficiency are key goals in modern orthopedics.
Purpose of the Study:
- To explore the potential of artificial intelligence (AI) in advancing value-based orthopedic surgery.
- To identify the mechanisms through which AI can impact orthopedic care: agency, automation, and augmentation.
- To outline the challenges and considerations for successful AI implementation in this field.
Main Methods:
- Literature review and conceptual analysis of AI applications in healthcare.
- Examination of AI's role in patient engagement, operational efficiency, and clinical decision-making.
- Identification of barriers to AI adoption in orthopedic surgery.
Main Results:
- AI enhances patient agency via improved health literacy and remote monitoring, reducing costs.
- AI-driven automation optimizes operating room scheduling and administrative tasks, improving efficiency.
- AI augmentation improves diagnostic imaging, surgical planning, and outcome prediction for personalized treatments.
Conclusions:
- AI presents significant opportunities to improve value-based care in orthopedic surgery through agency, automation, and augmentation.
- Successful integration requires addressing challenges like professional acceptance, data privacy, and financial investment.
- Careful attention to regulatory frameworks, data security, and clinical validation is crucial for AI adoption.
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