When Will AI Replace Chirurgiens orthopédiques, sauf pédiatrie ?

The impact of AI on orthopedic surgeons is unfolding gradually. While no definitive early, mid, or late stages are strictly defined by dates, significant automation is projected by 2032. Junior orthopedic residents and early-career surgeons in academic settings may experience AI-assisted training accelerating their learning curves as early as 2027-2028.

The initial wave of AI impact will focus on diagnostic activities. This includes AI-enhanced imaging for fracture detection, AI-driven recommendations for treatment protocols, and sophisticated surgical planning. Administrative tasks like documentation, coding, and managing prior authorizations are also prime candidates for significant automation.

Established surgeons in private practice and complex subspecialties, such as spine, joint reconstruction, sports medicine, and trauma, are expected to face minimal displacement through 2035 and beyond. The core of surgical execution and intraoperative decision-making will remain firmly in human hands.

The vulnerability score for orthopedic surgeons, except pediatric, is low at 0/100, indicating minimal direct job displacement. However, specific roles within the profession will experience greater AI influence. Junior orthopedic residents and those early in their academic careers might see AI tools significantly alter their training and initial practice.

Established surgeons in private practice and highly specialized fields like spine surgery, joint reconstruction, sports medicine, and trauma are in a highly secure position. The demand for these services is projected to increase by 25-35% by 2035 due to aging populations, ensuring continued need for human expertise.

The profession is evolving, not disappearing. To thrive, orthopedic surgeons should embrace AI as a tool. Focus on developing skills in areas AI cannot replicate: complex surgical execution, critical intraoperative decision-making, and empathetic patient counseling. Staying abreast of AI advancements in diagnostics and planning will be crucial.

For those in academic settings, leverage AI-assisted training to enhance learning curves. For all surgeons, proactively engage with AI technologies to understand their capabilities and limitations. This proactive approach will not only mitigate potential disruptions but also unlock new efficiencies and improve patient care outcomes.