From the Hospital for Special Surgery in New York, New York.
ARTIFICIAL INTELLIGENCE
7
AI-Driven Risk Stratification in Spine Surgery Myles R.J. Allen, MBChB
Artificial Intelligence (AI) is a broad field of machine-driven problem-solving, data analysis, and pattern recognition, which aims to mimic traditionally human cognition.1 As a subset of AI, machine learning is focused on simulating human processing mechanisms using data input and algorithms, which formulate specialized systems that can predict specific outcomes.1,2 Since its development in the 1950s, AI has rapidly expanded our knowledge and resources to better understand the world by sifting through large volumes of data to identify patterns and outliers and solve difficult problems.1 The widespread availability of AI systems such as Chatsonic or ChatGPT has allowed the public to incorporate AI into everyday life, including AI-powered assistants, fraud protection, personalized learning, and autonomous vehicles. These systems are typically used as an enhancement tool of skilled professionals, rather than a replacement of human workf low. As an adaptable and available tool, its use increases efficiency by increasing productivity, reducing time and capital expenditure, and provides information for a plethora of areas such as economic, social, and governmental structures. 3 As AI rapidly advances, many professionals show concern regarding its future applications and the possibility of the outright replacement of human labor. While these
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concerns are valid, we aim to reinforce the grand benefits of utilizing AI as an advancement tool regarding risk stratification in spine surgery.
Ashley Yeo Eun Kim, BA
Conventional vs AI Risk Stratification Spine surgery risk assessment remains challenging. ConvenOlivia C. Tuma, BS tionally, multiple risk assessment tools have been validated using patient factors and comorbidities. Certain metrics, including the American Society of Anesthesiologists classification, modified Tomoyuki Asada, MD Charlson Comorbidit y Index, and modified Frailty Index, have been retrospectively identified as important contributors for predicting surgical risks.4-6 However, these tools vary in accuracy Sravisht Iyer, MD and clinical applicability. For instance, Pulido et al demonstrated that adverse events can be predicted by the Modified Frailty Index, yet Lakomkin et al ascertained contrasting results.4,5 Additionally, Lakomkin et al established that the Charlson Comorbidity Index demonstrated superior predictive capacity over the Modified Frailty Index, but it could be used to predict mortality and length of
Vertebral Columns
Fall 2023
