**7.1 Cost and feasibility**

Successful integration of any new process or technology is dependent upon the ease of implementation, as well as the overall cost of the technology versus the revenue and benefit it generates. The United States leads in health care spending but has the worst outcomes when compared to nations such as Canada, Germany, the United Kingdom, Australia, Japan, Denmark, France, the Netherlands, Switzerland, and Sweden [152]. Health care spending is estimated to comprise nearly 20% of the US gross domestic product in 2025, which equates to \$5.3 trillion [145]. Neurosurgery is among the most expensive medical specialties, with the average procedure and hospitalization costing \$21,825 to \$22,924 depending on the volume of the medical center [153]. The cost of a spinal fusion is 12 times greater than it was 30 years ago [145]. This in combination with the emergence of value-based care and changing reimbursement patterns has led to increased research into cost saving methodologies. AI applications associated with this research include risk adjusted reimbursement models, predictive models of hospital length of stay, and predictors of patients more suitable for outpatient procedures. Within the neurosurgical realm, these studies have focused on spinal surgeries and there is a paucity of data on the intracranial surgical aspects of neurosurgery [154]. A meta-analysis of AI economic studies performed by Khanna et al. revealed that most of the research is focused on either diagnosis or treatment aspects throughout all medicine and the studies lack consideration of purchase and maintenance costs associated with AI, as well as few if any comparisons to alternative technologies [152].

Though investigation into the financial aspects of AI use in neurosurgery is on the rise, no study to date has produced a thorough net present value assessment within a large-scale experimental design [154]. Externally validated studies conducted on a larger scale with robust cost and net gain/loss calculations are necessary to accurately determine the feasibility and true value of the integration of AI into neurosurgery from a financial standpoint. This is particularly important being that the mean cost of an AI system ranges from \$20,000 to \$1 million, depending upon the system. The more complex the system, the greater the cost, albeit there are minimal viable products available in the \$8000 to \$15,000 price range [155].

Maintenance and continued operation represent a significant investment as well. AI systems require a staff of project managers, software engineers, data scientists, and software developers. A project manager will cost between \$1200 to \$4600 per month. Software engineers and data scientists contribute \$600 to \$1500 per day and \$500 to \$1100 per day in cost respectively. The annual salary of an in-house data scientist averages \$94,000 while a software developer has an annual salary of \$80,000 [156]. Additionally, health networks incur an average cost of \$15,000 to recruit candidates to fill these positions, as well as the cost to train the staff [156]. Outsourcing the maintenance and operation of the system offers a more frugal alternative to in-house staffing, however, there can be a lack of continuity and immediate availability with the remote staff.

Reimbursement for AI is still in its relative infancy as payers only began to approve coverage of AI use in late 2020 [157]. Currently, eight image-based
