Artificial Intelligence in Orthopedic Surgery

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Artificial Intelligence in Orthopedic Surgery: Current State and Future Perspective

From voice assistants to self-driving cars, artificial intelligence (AI) has become part of our everyday lives. But its impact goes beyond convenience—AI is revolutionizing medicine, especially orthopedic surgery. Today, AI helps surgeons diagnose fractures faster, plan surgeries more precisely, and even operate with robotic assistance. Let’s explore how AI is changing orthopedics, what it can do now, and where it’s headed next.

This article draws on a 2019 editorial in the Chinese Medical Journal by Xiao-Guang Han, MD, and Wei Tian, MD—leading experts from the Department of Spine Surgery at Beijing Jishuitan Hospital and the Beijing Key Laboratory of Robotic Orthopedics. Their work highlights AI’s current uses and future potential in orthopedic care.

What Is AI and Machine Learning?

AI refers to computer systems that simulate human intelligence (like reasoning or problem-solving). Machine learning (ML)—a key subset of AI—uses algorithms that learn and improve from data without explicit programming. There are two main types:

  • Supervised ML: Learns from labeled data (e.g., “this X-ray shows a fracture”) to predict outcomes for new patients.
  • Unsupervised ML: Finds hidden patterns in unlabeled data (e.g., grouping patients with similar knee pain symptoms) without prior training.

Current Applications of AI in Orthopedics

AI’s impact on orthopedics is wide-ranging, with three key areas of progress:

1. Medical Imaging: Faster, Safer, More Accurate

AI improves every step of the imaging process—from how images are taken to how they’re analyzed.

  • Acquisition: AI speeds up MRI scans (reducing patient time in the machine) and lowers CT scan radiation doses (cutting cancer risks). A 2018 study in Magn Reson Med found AI-powered MRI reconstruction maintains image quality while shrinking scan time.
  • Interpretation: AI doesn’t replace radiologists—it augments them. For example:
    • AI detects fractures (proximal humerus, wrist, vertebral compression) on X-rays as well as or better than orthopedic surgeons (Acta Orthop, 2018).
    • AI identifies hip/knee osteoarthritis on X-rays with accuracy matching expert radiologists (PLoS One, 2017).
    • AI labels lumbar disc abnormalities on MRI with 95.6% accuracy—on par with a seasoned radiologist (Eur Spine J, 2017).
    • AI improves bone age assessment: When combined with a radiologist, it’s more accurate than a radiologist alone (Skeletal Radiol, 2019).
  • Segmentation: AI automates “segmentation”—isolating specific structures (like knee cartilage) from MRI scans. A 2018 study in Magn Reson Med found deep learning models segment knee anatomy with high precision, helping track cartilage loss in osteoarthritis.

The catch? AI needs large, high-quality datasets to learn—which can be expensive and limit access in low-resource settings. But as technology advances, these barriers are fading.

2. Outcome Prediction and Decision Support

AI helps solve one of medicine’s biggest challenges: predicting patient outcomes. By analyzing clinical data (symptoms, lab results), genomic info, and images, ML models can:

  • Forecast post-surgical complications: A 2018 study in Spine Deform used ML to predict complications in adult spinal deformity surgery, helping surgeons tailor care.
  • Spot injury risks: ML analyzes sensor data to identify patterns linked to dynamic knee valgus (a movement that increases ACL tear risk), per a 2019 study in Musculoskelet Sci Pract.

Decision support systems are another win. For example, IBM Watson Health uses ML to help oncologists choose cancer treatments, and similar tools assist with low back pain: A 2008 study in Arthritis Rheum found AI classifies back pain patients more rigorously than humans alone, leading to better treatment matching.

These systems don’t replace doctors—they give them more data to make informed decisions. In the future, AI could help patients self-refer to the right services faster, improving access to care.

3. Robotic Surgery: Precision and Accessibility

Orthopedic robots have come a long way since 1992, when the ROBODOC system first assisted with total hip replacements. Today:

  • Joint replacements: Robots like the Mako system are standard for knee/hip arthroplasty, offering better limb alignment, shorter surgery times, and less blood loss than traditional methods.
  • Spine surgery: Robots like Renaissance and Rosa improve pedicle screw accuracy (critical for safety) and reduce radiation exposure for patients and staff.

But the TianJi Robot—developed by Han and Tian’s team—takes this further. Introduced in 2016, it’s a multi-indication robot that works for spinal, pelvic, acetabular, and limb fracture surgeries. It combines a robotic arm with real-time navigation, so surgeons place screws or implants with far greater precision than freehand techniques. A 2019 study in J Neurosurg Spine found TianJi significantly improves instrument accuracy and clinical outcomes.

In 2019, Prof. Tian made history by performing the world’s first 5G-enabled remote orthopedic surgery. Using 5G’s speed and low latency, he guided the TianJi Robot to operate on a patient in another location—paving the way for remote surgery to reach underserved areas. This combination of AI, robotics, and 5G could make high-quality orthopedic care accessible worldwide.

Limitations of AI in Orthopedics

Despite these advances, AI isn’t perfect. Key challenges include:

  1. Cost and Practicality: AI systems (and robots like TianJi) are expensive. They also require time to set up and use during surgery—something busy hospitals may struggle with. Most studies also lack long-term data on AI’s impact (e.g., do AI-guided joint replacements last longer than traditional ones?).
  2. Ethics: ML relies on big datasets, which raises privacy risks (e.g., patient data breaches). Liability is another issue: If an AI makes a mistake (like misdiagnosing a fracture), who’s responsible—the doctor, hospital, or AI developer?
  3. Autonomy: Today’s robots are “assistive,” not autonomous—they follow surgeon commands, not make decisions. Even advanced systems like TianJi need human oversight. While self-learning machines could perform independent tasks someday, there’s a risk clinicians might lose control.
  4. Governance: AI is evolving faster than laws and regulations. There’s no global standard for testing AI tools or protecting patients, which could put people at risk.

The Future of AI in Orthopedics

AI has already transformed orthopedic surgery—from faster imaging to more precise robots—but its true potential is still unfolding. The TianJi Robot and 5G remote surgery show how AI can make care safer and more accessible. But to reach its full promise, we need to:

  • Lower costs and improve practicality.
  • Solve ethical issues (privacy, liability).
  • Create better governance (laws to protect patients).

As Han and Tian note, “Despite its pitfalls, machine learning provides a unique ability to create meaningful change.” For patients, this means better outcomes. For surgeons, it means more tools to save lives. And for the future of orthopedics? AI isn’t just a trend—it’s the next frontier of care.

References

  1. Deo RC. Machine learning in medicine. Circulation 2015;132:1920–1930. doi.org/10.1161/CIRCULATIONAHA.115.001593
  2. Panchmatia JR, Visenio MR, Panch T. The role of artificial intelligence in orthopaedic surgery. Br J Hosp Med (Lond) 2018;79:676–681. doi.org/10.12968/hmed.2018.79.12.676
  3. Tack C. Artificial intelligence and machine learning: applications in musculoskeletal physiotherapy. Musculoskelet Sci Pract 2019;39:164–169. doi.org/10.1016/j.msksp.2018.11.012
  4. Gyftopoulos S, Lin D, Knoll F, Doshi AM, Rodrigues TC, Recht MP. Artificial intelligence in musculoskeletal imaging: current status and future directions. AJR Am J Roentgenol 2019;213:506–513. doi.org/10.2214/AJR.19.21117
  5. Lakhani P, Prater AB, Hutson RK, Andriole KP, Dreyer KJ, Morey J, et al. Machine learning in radiology: applications beyond image interpretation. J Am Coll Radiol 2018;15:350–359. doi.org/10.1016/j.jacr.2017.09.044
  6. Hammernik K, Klatzer T, Kobler E, Sodickson DK, Pock T, Knoll F. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med 2018;79:3055–3071. doi.org/10.1002/mrm.26977
  7. Chung SW, Han SS, Lee JW, Oh KS, Kim NR, Ypoon JP, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop 2018;89:468–473. doi.org/10.1080/17453674.2018.1453714
  8. Xue Y, Zhang R, Deng Y, Chen K, Jiang T. A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis. PLoS One 2017;12:e0178992. doi.org/10.1371/journal.pone.0178992
  9. Jamaludin A, Lootus M, Kadir T, Zisserman A, Urban J, Battié MC, et al. Issls prize in bioengineering science 2017: automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist. Eur Spine J 2017;26:1374–1383. doi.org/10.1007/s00586-017-4956-3
  10. Tajmir SH, Lee H, Shailam R, Gale HI, Nguyen JC, Westra SJ, et al. Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiol 2019;48:275–283. doi.org/10.1007/s00256-018-3033-2
  11. Zhou Z, Zhao G, Kijowski R, Liu F. Deep convolutional neural network for segmentation of knee joint anatomy. Magn Reson Med 2018;80:2759–2770. doi.org/10.1002/mrm.27229
  12. Kim JS, Arvind V, Oermann EK, Kaji D, Ranson W, Ukogu C, et al. Predicting surgical complications in patients undergoing elective adult spinal deformity procedures using machine learning. Spine Deform 2018;6:762–770. doi.org/10.1016/j.jspd.2018.03.003
  13. Hill JC, Dunn KM, Lewis M, Mullis R, Main CJ, Foster NE, et al. A primary care back pain screening tool: identifying patient subgroups for initial treatment. Arthritis Rheum 2008;59:632–641. doi.org/10.1002/art.23563
  14. Lang JE, Mannava S, Floyd AJ, Goddard MS, Smith BP, Mofidi A, et al. Robotic systems in orthopaedic surgery. J Bone Joint Surg Br 2011;93:1296–1299. doi.org/10.1302/0301-620X.93B10.27418
  15. Kochanski RB, Lombardi JM, Laratta JL, Lehman RA, O’Toole JE. Image-guided navigation and robotics in spine surgery. Neurosurgery 2019;84:1179–1189. doi.org/10.1093/neuros/nyy630
  16. Goradia VK. Computer-assisted and robotic surgery in orthopedics: where we are in 2014. Sports Med Arthrosc Rev 2014;22:202–205. doi.org/10.1097/JSA.0000000000000047
  17. Han X, Tian W, Liu Y, Liu B, He D, Sun Y, et al. Safety and accuracy of robot-assisted versus fluoroscopy-assisted pedicle screw insertion in thoracolumbar spinal surgery: a prospective randomized controlled trial. J Neurosurg Spine 2019;8:1–8. doi.org/10.3171/2018.10.SPINE18487
  18. Tian W. Robot-assisted posterior c1-2 transarticular screw fixation for atlantoaxial instability: a case report. Spine (Phila Pa 1976) 2016;41 (Suppl 19):B2–B5. doi.org/10.1097/BRS.0000000000001674
  19. Tian W, Fan MX, Liu YJ. Robot-assisted percutaneous pedicle screw placement using three-dimensional fluoroscopy: a preliminary clinical study. Chin Med J 2017;130:1617–1618. doi.org/10.4103/0366-6999.208251

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