Artificial Intelligence (AI) is reshaping health care, and spine care is no exception. Dr. Kiapour reviewed how AI is being integrated into diagnostics, surgical planning, and operational workflows.

1. Improved Diagnostic Accuracy: AI-driven tools, such as convolutional neural networks (CNNs), enhance the accuracy of diagnosing spinal conditions like stenosis and fractures. These tools analyze imaging data to detect abnormalities with high precision. Advanced systems like SpineNet automate vertebral body localization and grading of spinal degeneration, significantly improving the speed and accuracy of diagnoses. Predictive algorithms also help forecast postsurgical complications, aiding in effective risk stratification.
2. Personalized Care and Surgical Planning: Machine learning models allow for highly personalized treatment plans by analyzing patient-specific data, including imaging results and demographic information. These models predict the risk for suboptimal outcomes, such as the likelihood of chronic pain or the need for revision surgeries, enabling surgeons to tailor postoperative care. Additionally, AI-powered robotic systems enhance surgical precision, minimizing intraoperative complications and optimizing recovery trajectories.
3. Operational Efficiency: AI contributes to streamlined workflows through automation. For instance, AI-powered scribe tools reduce documentation burdens, freeing up clinicians to focus more on patient care. Triage bots and automated scheduling systems improve clinic operations by managing patient flow, reducing no-show rates, and optimizing resource allocation.

While promising, the integration of AI into spine care is fraught with challenges, specifically:

1. Algorithmic Bias: AI models often inherit biases from the datasets they are trained on, which can lead to disparities in care. Minority populations are frequently underrepresented in medical datasets, resulting in less accurate predictions and potentially inappropriate treatment recommendations for these groups.
2. Over-Reliance and Skill Degradation: As clinicians become increasingly dependent on AI tools, there is a risk of skill degradation. New medical professionals may lack essential diagnostic and decision-making experience if they overly rely on AI, potentially compromising the quality of care in scenarios where AI is unavailable or ineffective.

The ethical implications of AI in health care cannot be overlooked. Issues such as data privacy, the opacity of AI decision-making (“black box” problem), and legal accountability for AI-driven errors pose significant challenges. The use of AI in health care raises complex ethical and legal concerns.

1. Data Privacy and Security: AI systems rely heavily on patient data, raising significant privacy and security concerns. Unauthorized data access or breaches could lead to identity theft or the misuse of sensitive medical information, undermining patient trust.
2. Lack of Explainability: Many AI models function as “black boxes,” providing little insight into how they arrive at their conclusions. This lack of transparency can create trust issues among clinicians and patients.
3. Legal Accountability: Determining liability in cases of AI-assisted medical errors is a challenging task. Current legal frameworks often fall short in addressing scenarios where AI contributes to decision-making, raising questions about who is ultimately responsible—the clinician, the AI developer, or the health care institution.

AI is revolutionizing spine care, offering significant advancements in diagnostics, personalized treatment, and operational efficiency. However, its integration into clinical practice also presents notable challenges and ethical dilemmas. Dr. Ali Kiapour’s session comprehensively reviewed and provided a comprehensive overview of the promise and pitfalls of AI in spine surgery, highlighting both its transformative potential and the hurdles that must be addressed (Figure 1).