Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.

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BACKGROUND Osteoporosis is a metabolic bone disease that commonly affects the elderly. Degenerative spinal disease that may require surgical intervention is also prevalent in this susceptible population. If undiagnosed or untreated before spine surgery, osteoporosis may result in an increased risk of postoperative adverse events. Nontreatment of osteoporosis preoperatively may be related to a poor understanding of bone physiology, a lack of standardized treatment algorithms, limited cost-effective interventions, and reluctance by spine surgeons to be the primary provider of osteoporosis management. OBJECTIVE The objective of this evidence-based review is to develop guidelines for the preoperative assessment and treatment of osteoporosis in patients undergoing spine surgery. METHODS A systematic review of the literature was performed using the National Library of Medicine/PubMed database and Embase for studies relevant to preoperative diagnostic studies that predict increased risk of osteoporosis-related postoperative adverse events and whether the preoperative treatment of low bone mineral density (BMD) in patients with osteoporosis improves outcome. RESULTS Out of 281 studies, 17 met the inclusion criteria and were included for systematic review. The task force affirmed a Grade B recommendation that preoperative osteoporosis testing with a dual-energy X-ray absorptiometry scan (T-score < −2.5), a computed tomography scan (Hounsfield units <97.9), and serum vitamin D3 level (<20 ng/mL) predict an increased risk of osteoporosis-related adverse events after spine surgery. The task force determined a Grade B recommendation that preoperative osteoporosis treatment with teriparatide increases BMD, induces earlier and more robust fusion, and may improve select patient outcomes. There is insufficient evidence regarding preoperative treatment with bisphosphonates alone and postoperative outcome. CONCLUSION This evidence-based clinical guideline provides a recommendation that patients with suspected osteoporosis undergo preoperative assessment and be appropriately counseled about the risk of postoperative adverse events if osteoporosis is confirmed. In addition, preoperative optimization of BMD with select treatments improves certain patient outcomes. The full guidelines can be accessed at https://www.cns.org/guidelines/browse-guidelines-detail/3-preoperative-osteoporosis-assessment

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