February 10, 2026
4 min read
Orthopedic surgeons are living in an era of unprecedented technological advancement. Robotic-assisted surgery, AI-driven preoperative planning and patient-specific instrumentation have transformed the OR.
Robotic-assisted surgery promises submillimeter accuracy, reproducible implant placement and personalized reconstruction. We are guided by real-time feedback and sophisticated algorithms. Yet as we embrace these tools with enthusiasm, a thoughtful question can linger: Where is the high-quality evidence that the technological advances consistently translate into superior patient outcomes?
Clinical practice
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Many orthopedic surgeons have integrated these technological advancements into their clinical practices, drawn by reduced variability, shorter learning curves for complex procedures and the ability to execute plans once limited by human imprecision. Some orthopedic surgeons may contend that patient demand for robotic surgery is so high that its absence could limit their practice.
However, beneath all the enthusiasm is an important issue – the lack of prospective, randomized data linking specific mechanical or mathematical parameters assigned to the robot to measurable improvements in patient function, satisfaction and reconstruction longevity. The absence of this data, combined with the increased cost of robotic surgery, leaves open the question of whether we are truly providing value to patient care.
Reverse shoulder arthroplasty has revolutionized the management of rotator cuff arthropathy and complex fractures. One debated parameter is glenosphere lateralization. Conventional wisdom, which is supported by surgeon experience, biomechanical studies and retrospective reviews, suggests increasing lateralization improves postoperative range of motion, enhances deltoid tension and reduces the risk for scapular notching without increasing complications, such as acromion fractures or instability. Many systems now offer modular options to achieve precise lateral offsets, with commonly suggested targets of 6 mm to 8 mm for men and 4 mm to 6 mm for women.
The foundation for these parameters is limited within the scientific literature. Most recommendations are from cadaveric models, finite element analyses or observational cohorts rather than prospective randomized controlled trials. Individual patient factors, such as bone quality, soft-tissue envelope, preoperative deformity and activity demands, introduce variability that current planning algorithms may oversimplify.
When surgeon experience is added to the equation, the analysis becomes even more complex. A highly skilled surgeon using conventional instrumentation may outperform a less experienced colleague relying on robotics if the robotic plan is based on beliefs that may lack strong evidence.
This pattern repeats across orthopedics. In TKA, robotic systems enable one to target neutral, kinematic or restricted kinematic alignment with precision. Proponents of each alignment cite retrospective data or short-term outcomes to support their approach, but long-term randomized controlled trials comparing the strategies head-to-head with robotic execution standardized across all study group participants remain inconclusive about what method is superior when assessing individual patient outcomes.
The data are emerging, but not yet conclusive. Many studies have selection and surgeon bias, industry funding or short follow-up.
Precision with accuracy
The issue is not that technology is ineffective at enhancing reproducibility, achieving preoperative goals and lowering complication rates. Rather, the issue is our collective tendency toward belief in something without definitive evidence. For example, because we are using a robot and our procedures are more precise, we believe outcomes must be better.
We allow sophisticated software to dictate targets that feel authoritative because they were derived from large datasets or biomechanical models, yet the datasets may lack the granularity needed for true personalization. We risk merging precision with accuracy, believing that hitting a predetermined parameter perfectly guarantees it was the right parameter for that patient.
There needs to be a fundamental shift in how we approach innovation. As specialists, orthopedic surgeons need more large-scale, multicenter prospective randomized controlled trials that isolate specific technological parameters and measure their impact on meaningful outcomes. Joint registries are a step in the right direction, but they must evolve to capture detailed preoperative patient and intraoperative data along with the long-term patient-reported and radiographic outcomes.
We should harness AI for execution and discovery. Machine learning algorithms trained on multi-institutional datasets could identify patterns we may have missed, helping to determine which combination of lateralization, glenoid inclination and humeral version truly optimizes deltoid efficiency in each patient. Preoperative imaging, such as advanced CT with 3D reconstruction, dynamic MRI or wearable sensor data could feed predictive models that simulate postoperative function before any incision is made. Planning software should not only execute a surgeon’s preconceived plan but also challenge it with evidence-based predictions based on thousands of cases with similar preoperative parameters and match surgical procedures to evidence-supported outcomes.
We must acknowledge the enduring importance of surgeon judgment. Technology is a tool, not a replacement for experience. The best outcomes will likely come from orthopedic surgeons who use robotics and AI as partners, critically evaluating algorithmic suggestions against their own clinical acumen and individual patient.
Innovators by nature
As orthopedic surgeons, we are innovators by nature. We adopted arthroscopy, biological augmentation, navigated surgery and now robotics with remarkable speed. Rapid adoption without rigorous validation risks perpetuating practices that look impressive on a screen yet fail to improve outcomes for our patients. The true promise of technology lies not in achieving arbitrary numerical perfection but in answering questions we have been asking for decades.
We need to embrace skepticism so we can move beyond enthusiasm and blind faith. We need to design and support studies that test our assumptions and collaborate across institutions and industry to build better data ecosystems. We need to ask the right questions to generate evidence of value for our patients, rather than accepting new technology as inherently better than status quo. It is only then that we will close the gap between technological capability and clinical reality. This practice will ensure our advancements deliver what matters most, leading to healthier, happier patients.
For more information:
Anthony A. Romeo, MD, is the Chief Medical Editor of Healio | Orthopedics Today. He can be reached at Healio, 6900 Grove Road, Thorofare, NJ 08086; email: orthopedics@healio.com.
Sources/Disclosures
Source:
Expert Submission
Disclosures:
Romeo reports receiving royalties from, being on the speakers bureau and a consultant for, and doing contracted research for Arthrex.
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