AI in Clinical Orthopaedics: Practical Applications in 2025

For years, Artificial Intelligence (AI) was a buzzword discussed at conferences in the context of "future potential." In 2025, that potential has been realized. AI is no longer just a research tool; it is a clinical instrument as vital as the stethoscope or the scalpel.

This article focuses on the tangible, clinical applications of AI that orthopaedic surgeons are using today to improve diagnostic accuracy, surgical precision, and patient outcomes.

1. Computer Vision: The Automated Eye

Computer Vision (CV) is the field of AI that trains computers to "see" and interpret images. In orthopaedics, this means reading X-rays, CTs, and MRIs.

Automated Fracture Detection

Emergency Departments are high-pressure environments where missed fractures are a leading cause of litigation.

• The Technology: Convolutional Neural Networks (CNNs) trained on millions of labeled radiographs.
• Clinical Application: Software like Rayvolve or Gleamer now sits as a layer on top of the PACS system. When an X-ray is taken, the AI analyzes it in seconds. If a fracture is detected, it places a bounding box or "heat map" over the area to alert the clinician.
• Performance: Studies show these algorithms match or exceed the sensitivity of senior radiologists for subtle fractures (e.g., scaphoid, femoral neck, radial head).
• The Benefit: It acts as a safety net, preventing the "missed hip fracture" sent home with a diagnosis of sciatica.

Opportunistic Screening

Orthopaedic surgeons order thousands of CT scans for trauma or spine pathology.

• Bone Density: AI algorithms can automatically analyze the Hounsfield Units of vertebral bodies on a routine chest/abdomen CT to calculate bone mineral density (BMD) with high correlation to DEXA.
• Sarcopenia: Similarly, algorithms can measure psoas muscle cross-sectional area to assess frailty, predicting surgical risk without any additional radiation or cost.

2. Pre-operative Planning and Templating

The days of acetate templates and marker pens are over.

Automated Segmentation

Creating a 3D model of a bone from a CT scan previously took an engineer hours of manual "thresholding."

• The AI Shift: Deep learning models now auto-segment bones, separating the femur from the pelvis or the tibia from the talus in seconds.
• Application: This enables rapid production of Patient Specific Instrumentation (PSI) or 3D printed models (see our 3D Printing topic).

Intelligent Templating

AI software recognizes landmarks (calcar, greater trochanter, lesser trochanter) on a calibrated X-ray.

• Workflow: It automatically suggests the optimal implant size, offset, and neck cut level.
• Accuracy: Current systems predict the correct implant size within +/- 1 size in >95% of cases, streamlining inventory management.

3. Robotics and Intra-operative AI

Robots like the Mako (Stryker) or ROSA (Zimmer Biomet) are essentially physical manifestations of AI planning.

Augmented Reality (AR) Navigation

While not strictly "AI" in isolation, AR headsets (like HoloLens) use computer vision to register the patient's anatomy in real-time.

• Spine Surgery: The surgeon sees the pedicle screw trajectory overlaid on the patient's skin. The AI tracks the patient's breathing and movement, adjusting the hologram instantly.
• Tumor Surgery: "See-through" vision allows the surgeon to visualize the tumor margins deep within the soft tissue.

Predictive Balancing

In Total Knee Arthroplasty (TKA), AI algorithms analyze the tension in the ligaments throughout the range of motion.

• The Insight: Instead of just aiming for a straight mechanical axis, the robot suggests bone cuts that will achieve "balanced gaps" based on thousands of prior successful cases. It moves from "measured resection" to "predictive balancing."

4. Predictive Analytics and Risk Stratification

This is "Moneyball" for medicine.

The "Crystal Ball" of Complications

Machine Learning models can ingest a patient's entire electronic health record (EHR)—labs, comorbidities, medications, vitals.

• Prediction: The model calculates a personalized risk score for specific complications: "This patient has a 12% risk of PJI and a 40% risk of needing a transfusion."
• Intervention: This triggers specific optimization protocols (e.g., pre-op iron infusion, nasal decolonization) that might be missed by a generic checklist.

Predicting LOS (Length of Stay)

Hospitals use ML to predict discharge dates on admission. This allows social work and physio to start planning on Day 0, reducing bed block.

5. Wearables and Gait Analysis

AI has moved out of the hospital and onto the patient's wrist.

• Remote Monitoring: Smartwatches (Apple Watch, Fitbit) collect continuous data on steps, gait symmetry, and sleep.
• Post-op Recovery: AI algorithms detect deviations from the expected recovery curve. If a TKA patient's step count plateaus at week 3, the surgeon gets an alert: "Possible stiff knee or infection." This allows for early intervention before the 6-week follow-up.

Conclusion

The "AI-Augmented Surgeon" is not a concept for the next generation; it is the standard for the current one. These tools do not remove the need for surgical skill or clinical judgment. Instead, they remove the cognitive load of routine tasks (finding the fracture, sizing the stem) and provide data-driven insights to inform complex decisions.

Clinical Pearl: When using AI templating or fracture detection, always treat the AI output as a "second opinion." Never let the algorithm override your clinical exam. If the AI says "No Fracture" but the patient has point tenderness on the scaphoid, treat it as a fracture.

References

1. Adams, S. J., et al. (2019). "Computer vs Human: Deep Learning versus Perceptual Training for the Detection of Neck of Femur Fractures." Journal of Medical Imaging.
2. Goksel, F., et al. (2022). "Artificial Intelligence in Orthopaedic Surgery: Current State and Future Directions." Arthroscopy.
3. Ramkumar, P. N., et al. (2019). "Remote Patient Monitoring Using Mobile Health for Total Knee Arthroplasty: Validation of a Wearable and Machine Learning-Based Surveillance Platform." Journal of Arthroplasty.