Machine vision and machine learning (ML) models have humans beat on speed, accuracy, and repeatability. There is even a machine learning technique that quantifies how confident it is in its own model's predictions. Think of it as a friend that removes the nervous sweat bead from your forehead when tough decisions are on the table. So when a company needs to assess risk, ML models go a long way in narrowing the window of probability. This means making more informed decisions, achieving better outcomes, automating time-consuming tasks, and lowering costs can all be within reach. Streamlining AI technologies like these also allows human leaders to step back and focus on the end-to-end impact and timeliness of risk assessment from a macro view. It's probably time to consider what you are risking if your decisions are not data-driven. Hopefully not somebody's spine.

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SBD is now rigorously testing their MVP machine vision solution with researchers at esteemed institutions such as Emory, Rush, and Johns Hopkins. The collaboration aims to further validate the solution's effectiveness in reducing reherniation risks and improving patient outcomes. Healthcare providers can anticipate significant cost savings and enhanced operational efficiency, positioning the MVP as a promising step towards revolutionizing spine surgery and patient care.