Transforming medical device reliability with AI-powered models

November 13, 2025

In every industry, preventing equipment downtime and maintaining operational continuity is a top priority. Preventive maintenance—making rounds, doing routine maintenance, and logging data—has long been a manual, tedious process, but with recent advances in AI, predicting and preventing failures and downtime is becoming more manageable than ever before.

Tiger Analytics is a solutions integrator and Microsoft partner that implements AI-powered solutions. The organization has all three Solutions Partner designations for Azure, as well as three specializations: AI Platform on Microsoft Azure (formerly AI and Machine Learning on Microsoft Azure), Build AI Apps on Microsoft Azure, and Analytics on Microsoft Azure. They believe in using AI to transform operations for customers across industries. Their commitment to using AI to develop smart solutions empowered them to help a medical device company develop a predictive maintenance solution for their radiation therapy devices.

Preventing downtime in a critical healthcare device

Tiger Analytics’ customer makes radiation therapy systems for cancer patients. Operational downtime in any industry is a challenge, but downtime for complex medical devices could lead to significant disruptions in patient treatment schedules, potentially impacting not just operational efficiency for healthcare providers but also, and more importantly, patient well-being. These disruptions can result in substantial financial losses due to expedited part replacements and even potential reputational damage.

To offset these risks, the company decided to develop a predictive maintenance practice in the hopes of mitigating equipment failure and enhancing device reliability and customer trust. They had two main goals:

• Predictive failure identification: Accurately predict device component failures before they happen using machine logs to perform proactive maintenance and minimize downtime.
• Logistics optimization and cost reduction: Create better overall system health monitoring to reduce the need for costly, time-consuming emergency replacements. Additionally, facilitate the shipment of replacement parts via ground rather than expensive, last-minute air freight.

Turning large volumes of unstructured data into essential insights

To tackle these challenges and deliver a solution that worked, Tiger Analytics took a threefold approach: data extraction, pre-processing and feature engineering, and model training and inference. This process involved a significant amount of experimentation and fine-tuning to discover which features are highly correlated with failures, determine the best hyperparameters to train the model, and validate the model on different holdouts and incremental loads.

Tiger Analytics developed a reliable model by leveraging Microsoft Azure and its robust capabilities to effectively support and accelerate solution delivery. Tiger Analytics used Azure Databricks as the core platform and utilized PySpark for distributed data processing and model training. They also deployed Azure Blob Storage for data lake storage and Azure Machine Learning for model registration, deployment, and monitoring. Finally, they employed Microsoft Power BI to create dashboards to visualize the data after it had been processed, empowering the customer to access insights in a digestible, interactive manner.

“This project stood out because of its meaningful impact—not just on operational efficiency, but on patient care," said Dhaval Mukhatyar, partner at Tiger Analytics. "By helping the customer anticipate issues, we supported more reliable treatment schedules and a better experience for both healthcare providers and the people they serve.”

The results speak for themselves. The customer reported the following:

• Achieved 80% accuracy in predicting replacement needs.
• Significant cost savings through logistics optimizations.
• Significant reduction in appointment rescheduling due to equipment failure.

The Tiger Analytics solution made a meaningful difference in the company’s maintenance operations—and patients’ medical experiences, a win that can’t be quantified. Building upon this success, Tiger Analytics will continue to deliver smart, AI-powered solutions that solve real-world problems.