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Artificial Intelligence in Global Healthcare

AI and machine learning predict device maintenance, slashing servicing costs.


In the modern world, technology is important in many industries, like healthcare, and needs good customer experience.

Artificial intelligence has revolutionized problem-solving techniques, and this case study explores its application in reducing downtime for frequently used eye laser surgery equipment.

The client needed an AI consulting firm. Their budget: $30 - $50K - this is greatly dependent on the amount of data available for model development. Project took several months to complete.


The focus of this case study centers around the problem of downtime in eye laser surgery equipment. The frequent breakdowns and maintenance requirements have resulted in expensive callouts and inconvenience in rescheduling patient appointments.

Equipment is crucial for improving vision and treating eye conditions. However, when the equipment malfunctions, it leads to longer waiting times for patients and increased expenses for medical facilities.


To address these challenges, companies have implemented AI systems that identify patterns and predict potential equipment failures. Using AI and sensor data, they can predict maintenance needs and take proactive measures for maintenance.

Artificial intelligence (AI) assists in optimizing the scheduling of patient appointments, considering the availability of equipment. This prevents unnecessary rescheduling and reduces downtime for both patients and medical staff.


The implementation of AI and machine learning in predicting maintenance needs and potential device failures has significantly reduced costs associated with servicing devices. By proactively scheduling maintenance, clients can avoid reactive and costly repairs. The solution has eliminated patient rescheduling and business interruption.

Thanks to artificial intelligence and machine learning, clients can predict device problems ahead of time. Therefore, they can plan maintenance without causing issues for patients.