Tube Watch utilizes digital twin modeling with integrated artificial intelligence, machine learning, and software analytics to remotely monitor and analyze tube health trends in real time, ensuring optimal system performance and helping to avoid unplanned downtime.

With the power of machine learning, predictive analytics leverages data to simulate real-world scenarios. By accurately predicting the estimated failure date, it assists in making informed decisions about whether or not to proactively replace the tube and helps to reduce patient and staff stress as a result of canceled exams and procedures.

Keep equipment running with proactive part delivery and service scheduling. A GE HealthCare engineer remotely connects into the system to run an in-depth assessment and, if needed, will schedule on-site maintenance at a time that is most convenient for the customer.

Experience a smooth transition as the system returns to full functionality with minimal disruption and minimal impact on workflow after the tube and x-ray generation chain component replacement.