Artificial intelligence (AI) and machine learning are increasingly shaping orthopedic surgery by enabling pattern recognition, predictive analytics, and data-informed clinical decision support across large and complex datasets. Machine learning, as a subset of AI, offers opportunities to enhance risk stratification, imaging interpretation, perioperative planning, and workflow efficiency by identifying associations that may not be apparent through traditional clinical assessment. However, these technologies do not establish causation and therefore must be used to augment—rather than replace—surgeon judgment. A central theme is the need to distinguish clinically meaningful innovation from marketing-driven hype, with experts emphasizing transparency, reproducibility, external validation, and demonstrated relevance to patient outcomes as prerequisites for adoption. Ethical considerations, including bias, equity, and interpretability, are highlighted as essential components of responsible implementation.

Orthopedics is presented as a specialty well positioned to lead AI integration due to its structured workflows, standardized procedures, and access to longitudinal, measurable outcomes through registries, implants, robotics, and wearable technologies. The discussion underscores the importance of surgeon involvement as co-creators of AI systems to ensure clinical relevance and ethical alignment, rather than passive acceptance of externally developed tools. Education in AI literacy across all career stages is emphasized as critical, with training focused on understanding algorithm development, limitations, and appropriate clinical use. Ultimately, AI is framed as a force for augmentation rather than substitution, with its long-term value dependent on preserving human judgment, patient trust, and professional accountability while leveraging technology to improve care delivery.