So far, Macht believes the FDA has struck a fair balance between innovation and oversight—permitting devices to adapt to real-world data while keeping self-learning algorithms in check. Still, she says, cybersecurity, data privacy, and long-term safety remain areas that warrant sharper focus.

A secure future?
Movement on the cybersecurity front may come sooner than expected, especially if Aaron Hanna has anything to say about it. As chief technology officer at NVRT Labs, Hanna is working to reshape how HTM approaches both training and security. Once part of the College of Biomedical Equipment Technology, NVRT Labs now offers extended reality training for biomedical equipment technicians, or BMETs.
That focus on safety extends to NVRT’s work with AI, a technology Hanna believes could transform not only how devices are maintained, but how risks are mitigated. While the company hasn’t yet released AI-powered predictive maintenance tools, Hanna says development is well underway, with prototypes aimed at enhancing BMET training and streamlining on-the-job tasks.

“Predictive maintenance has huge potential,” he says. By tapping into sensor data, service logs, and equipment histories, AI could autonomously assess device health and forecast repairs — reducing downtime, boosting safety, and extending equipment life.

But AI’s value doesn’t end there. Internally, Hanna says, it can automate routine tasks defined by standard procedures, freeing HTM professionals to focus on more complex, high-value work. “Personally, AI has multiplied my daily output several times over,” he adds.

Proactive maintenance, powered by AI
When it comes to optimizing equipment life cycles, Hanna calls AI a game-changer. The industry is shifting, he says; moving away from reactive, calendar-based servicing toward proactive, data-informed strategies. Instead of waiting for devices to fail or relying on fixed schedules, AI empowers HTM teams to anticipate issues before they arise.

Hanna notes that AI can “anticipate failure by monitoring real-time telemetry against historical data” — a level of oversight that allows it to catch red flags human eyes might miss. Beyond that, AI can balance asset use by analyzing workload and suggesting reassignments, helping healthcare organizations maximize equipment across facilities. It can also prioritize biomed tasks by flagging high-risk devices, he says, ensuring urgent issues get immediate attention.

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