Complex spine surgery involves careful coordination across disciplines, anticipation of complications, and decision-making incorporating numerous clinical and radiographic variables. As artificial intelligence (AI) rapidly enters spine care — from preoperative planning software to intraoperative navigation and postoperative monitoring—there is growing debate about how these tools should be used, and just as importantly, where their limits lie.
Conceptually, complex spine surgery more closely resembles a team sport than a solo performance. As in all elite teams, success depends not on replacing leadership with analytics, but on integrating data intelligently while preserving human judgment.
Spine Surgery Is Already a Team Sport
In high-level spine care, outcomes are determined long before the incision and long after skin closure. While the surgeon typically leads the team, success hinges on contributions from anesthesia, neuromonitoring, nursing, advanced practice providers, rehabilitation teams, radiology, and medical consultants managing frailty, bone health, and comorbidities.
Intraoperatively, team members including anesthesia, nursing and neuromonitoring must perform in sync with high-level communication and real-time feedback. Postoperatively, care teams must detect early deviations from expected recovery to prevent compounding complications. Even subtle failures in communication or coordination can cascade into significant complications.
In such a detail-oriented, fast-paced environment, AI does not replace any individual role. Instead, it functions best as an additional layer of support — similar to an analytics department in professional sports — enhancing preparation, execution, and review without full on play-calling.
Where AI Fits Well: Preparation, Pattern Recognition, and Precision
Preoperative Planning: AI as the Team Analytics Department
Preoperative planning is one of the most promising areas for AI integration. Adult spinal deformity surgery involves balancing alignment goals, mechanical durability, and patient-specific risk. Traditional planning tools rely heavily on static radiographic parameters — Cobb angles, sagittal vertical axis, pelvic incidence–lumbar lordosis mismatch. While foundational, these metrics alone fail to capture the complexity of real patients.
AI’s real strength in surgical planning lies in its ability to aggregate large volumes of heterogeneous data. Emerging tools and planning software can integrate radiographic alignment, bone density, muscle mass, frailty indices, age-adjusted targets, and comorbidities to estimate complication risk or likelihood of mechanical failure. In doing so, AI helps surgeons identify patterns that may not be obvious at the individual level.
Importantly, this does not mean AI should decide who is or is not a surgical candidate. Rather, it should inform surgeons about risk tradeoffs — highlighting where a plan may push the limits of biology or where a less aggressive strategy may better align with patient goals.
Intraoperative Support: AI as Equipment Technology
Intraoperatively, AI-enabled technologies function much like modern equipment in elite sports. Carbon-fiber poles do not vault for Olympic athletes, and GPS wearables do not decide tactics for professional soccer teams — but both reduce variability, improve feedback under pressure, and allow skilled performers to execute consistently at the highest level. Similarly, navigation and robotic systems do not perform surgery; they enhance the precision and reproducibility of preoperative plans while reducing variability and fatigue-related error.
Despite these advances, AI remains most limited in the intraoperative setting. While navigation can optimize screw trajectories and robotics can guide instrumentation placement, there is substantial opportunity for innovation in real-time monitoring of correction goals. The next major breakthrough will be alignment systems capable of detecting under- or over-correction across multiple planes, improving radiographic reproducibility and expanding the number of surgeons able to safely perform complex deformity corrections.
No matter how advanced these technologies become, they cannot substitute for surgical judgment. When unexpected anatomy, bleeding, or neurologic changes arise, a human surgeon — not software — must adapt in real time. AI may reduce technical variability, but it cannot eliminate judgment error or assume responsibility for outcomes.
Postoperative Monitoring: AI as Film Review
Just as top teams review plays with film extensively, postoperatively, AI-driven analytics can help identify patients deviating from expected recovery trajectories. Early detection of complications, risk stratification for readmission, and longitudinal tracking of patient-reported outcomes are all promising applications.
At a systems level, these tools allow surgeons and institutions to learn from aggregate experience. Over time, the feedback loop can refine surgical planning, improve patient counseling, and identify best practices. Similar to film review in sports, AI-supported postoperative analysis helps surgical teams improve. However, the quality of analysis depends critically on the granularity of the data inputs. Centralized data repositories, data sharing and increasing contributions by individual centers and surgeons will ultimately drive improvements in data quality and AI-supported decision analyses. As always, these AI driven insights must be interpreted thoughtfully in conjunction with human reasoning and oversight.
Where AI Falls Short: Judgment, Context, and Moral Weight
Despite its strengths, AI has clear and important limitations.
Surgical Judgment Cannot Be Automated
Complex spine surgery is filled with tradeoffs that resist algorithmic solutions. Two patients with identical radiographs may require entirely different approaches based on functional goals, pain tolerance, social support, or willingness to accept risk.
While AI can estimate probabilities, it cannot determine whether a specific risk is acceptable to a particular patient. That judgment rests with the surgeon and patient together.
AI Does Not Understand Values
Informed consent is not a data problem — it is a human one. Patients bring fear, hope, family dynamics, and personal priorities into the decision-making process. In end-stage deformity cases especially, the decision to operate—or not—carries profound ethical and emotional weight.
Despite progression of AI and enabling technologies, surgeons will continue to bear the full responsibility of surgical outcomes.
Over-Reliance Risks Deskilling the Team
Particularly for newer trainees, there is growing concern that over-reliance on automation could erode fundamental skills. As workflows increasingly replace anatomy for residents and fellows, and trainees increasingly over-rely on AI outputs without understanding inputs, adaptability suffers. When technology fails — as it inevitably will — the team must have the ability to adapt and function at a high level.
Technology must elevate expertise without replacing it.
The Surgeon’s Role in the AI Era: Still the Team Captain
In elite sports, the best teams use analytics aggressively—but they do not surrender leadership to spreadsheets. Coaches and players understand the data, contextualize it, and decide when to deviate from it.
Similarly, surgeons must remain responsible for curating AI tools, understanding their limitations, and deciding when to rely on them and when to override them. This requires a new kind of literacy — not programming expertise, but comfort with uncertainty, probability, and bias.
Leadership in the AI era means protecting patient-centered decision-making while embracing tools that improve consistency and safety.
The Spine Team of the Future
The future of complex spine surgery is not surgeon versus AI — it is surgeon plus AI within a coordinated team. Teams that excel will integrate predictive analytics into preoperative planning, precision tools into execution, and continuous feedback into postoperative care.
Success will not be measured by faster surgeries or more sophisticated software, but by better decisions, fewer avoidable complications, and outcomes that align with patient goals.
