From faster fracture detection to robot‑guided surgery, artificial intelligence is beginning to reshape orthopedics—but big data gaps remain.

A new review in the International Journal of Surgery says artificial intelligence (AI) is moving from pilot projects to practical tools across orthopedic care, a field that accounts for a huge share of clinic visits worldwide and faces millions of fractures annually. The authors note, for example, that osteoporosis affects more than 200 million people globally and contributes to over 9 million fractures each year—problems that AI could help clinicians catch earlier and treat more precisely.  

What AI is already doing well

• Spotting fractures and other injuries: Across studies, AI programs identified fractures, bone tumors and osteoporosis with 5–10% higher accuracy than general practitioners, and in one X‑ray study boosted sensitivity from 80.8% to 91.5% and specificity from 87.5% to 93.9%. That kind of lift can reduce missed injuries and speed time to treatment.
• Reading complex knee MRIs: Deep‑learning systems such as “MRNet” classified ligament and meniscus injuries on MRI and helped predict diagnoses, pointing to faster, more consistent imaging reads.  
• Helping surgeons plan and operate: AI‑enabled planning tools (for example, AI‑HIP for total hip replacements) and surgical robots (such as Tianji, ROSA, MAKO, NAVIO) are improving implant sizing and placement and reducing complications. In one setup, an AI‑driven workflow cut the probability of intraoperative hemorrhage by ~87% during fracture procedures.
• Monitoring during complex cases: AI can track tumor changes and blood supply around fracture sites in real time—supporting on‑the‑fly decisions in long, high‑risk operations.  

Why it matters

Missed or late fracture diagnoses remain a persistent problem, particularly when symptoms are subtle. Emergency departments still miss an estimated 3.3% of fractures on first read—an opportunity for AI to act as a second set of eyes.  

Not a silver bullet (yet)

The review stresses that today’s systems can stumble on uncommon injuries or under‑represented patient groupsbecause many algorithms learn from small or imbalanced datasets. Much of the needed medical imaging still requires painstaking manual labeling by clinicians, and health data remain fragmented across hospitals, making it hard to build robust, generalizable models. In short: AI helps, but it doesn’t replace clinical judgment.

What’s next

The authors expect “all‑in‑one” orthopedic robots, micro‑robots to target bone infections, and richer combinations of AI with virtual/augmented reality and genomics to personalize care—from predicting who is most at risk of osteoarthritis to tailoring rehab exercises after surgery. (The review’s figures visualize current systems like Tianji/ROSA/AI‑HIP and a multi‑network pipeline that guides hip procedures.)

Bottom line for patients: AI is already helping radiologists and surgeons catch more, cut less, and plan better. As hospitals share data and teams validate tools across diverse populations, expect these gains to reach more clinics—and more patients—without losing the human expertise that keeps care safe.

Source: Guan J., Li Z., Sheng S., et al. “Artificial intelligence in orthopedics,” International Journal of Surgery (2025).