Advancements in artificial intelligence (AI) technology are revolutionizing nearly every industry including health care. AI has shown great promise in improving diagnostics, surgical outcomes and decision support in different medical specialties. In the field of spine surgery, AI holds significant potential to enhance pre-operative planning, intra-operative guidance and post-operative analysis.
By embracing AI on a broader scale, it is projected that the U.S. healthcare sector could experience significant cost savings, amounting to approximately $200 billion to $360 billion annually in 2019 dollars. These estimates consider realistic AI applications that utilize existing technologies over the course of the next five years. The integration of AI also presents the potential for non-monetary advantages, including enhanced healthcare quality, expanded access, improved patient experiences and increased satisfaction among healthcare professionals.
Current State-of-the-Art of AI in Surgery
The current state-of-the-art of AI in surgery primarily revolves around AI-based diagnosis, pre-operative planning and predictive outcomes. AI models trained on large datasets of radiological images, such as CT scans, are capable of accurately diagnosing various conditions, often outperforming human experts. These models enable faster and more precise identification of abnormalities, facilitating early intervention and personalized treatment plans. While these applications of AI in surgical treatment show tremendous promise, the technology is not yet being widely utilized due to a variety of data concerns and a complex regulatory framework.
Robotic vision is another area of AI application in surgery, although it is more developed in abdominal surgeries than spine surgery. Using advanced sensing technologies, robots can assist surgeons during procedures, enhancing precision and reducing invasiveness. However, AI-based decision support tools (DSTs) specifically designed for spine surgery are currently limited due to the scarcity of intra-operative imaging data.
Realizing the Potential of AI in Spine Surgery
Intra-operative data, including surgical video data, is crucial for training AI models that can provide real-time decision support during spine surgeries. However, up until recently, the lack of accessible and comprehensive intra-operative imaging data has hindered the development of effective AI applications in this domain.
Spinal navigation technology that utilizes light field technology and a multi-sensor suite is able to collect intraoperative surgical data. While the light field technology that makes this possible has existed for years in applications ranging from satellites to photography, its foray into the operating room is a comparatively recent development. It enables surgical video data capture and detailed visualization of the surgical site, which creates informative AI models and data in the form of a digital anatomical twin. This type of visualization allows the surgeon to simulate how a patient might respond to a particular treatment. The future of AI in spine surgery belongs to those who can obtain and analyze intra-operative surgical data.
Future Prospects and Challenges
The future of AI in spine surgery relies heavily on the availability of intra-operative surgical data. Access to comprehensive and high-quality data will facilitate the training of AI models capable of assisting surgeons during procedures, optimizing surgical outcomes and improving patient safety. Real-time decision support tools based on AI algorithms can enhance the accuracy and efficiency of spine surgeries, leading to better patient outcomes.
However, challenges such as data privacy, data collection protocols and computational requirements must be addressed for the widespread implementation of AI in spine surgery. Collaborative efforts between researchers, surgeons and technology developers are essential to overcoming these challenges and to unlock the full potential of AI in this field.
While AI is already making significant contributions to pre-operative planning and post-operative analysis, its utilization intra-operatively remains a key area for further development. Collecting and utilizing comprehensive intra-operative surgical data, as facilitated by innovative applications of existing technologies such as light field, will pave the way for advanced AI applications in spine surgery. By harnessing the power of AI, surgeons can enhance their decision-making capabilities, leading to safer and more effective spine surgeries in the future.