By Keri Forsythe-Stephens

From predictive maintenance that flags equipment issues before they occur to smarter asset management, AI has the potential to reshape how HTM professionals maintain technology.

But the shift requires more than technical know-how. Today’s HTM teams must make sense of complex data, manage interoperability, and stay one step ahead of hackers. And while AI can streamline operations, it also introduces new risks: black-box algorithms, murky accountability, and greater digital exposure.

For HTM, the potential is real — but so is the complexity. That’s why experts stress ongoing education, strong collaboration across healthcare teams, and clear governance to ensure AI helps, rather than hinders, clinical and operational goals.

A new skill set for a new era
Understanding AI starts with semantics, says Betsy J. Macht, a global supply chain leader and adjunct doctoral methodologist at Walden University’s College of Management and Human Potential. For one, AI and machine learning aren’t interchangeable. AI makes decisions based on human-defined goals, she explains, while machine learning refines those decisions by training on data.

For HTM professionals — who, according to Macht, “drive many aspects of the medical and adjacent fields” — that distinction matters. Once the domain of IT, AI now sits firmly in HTM’s wheelhouse, propelled by the rise of internet-connected devices. With that shift comes added responsibility. Macht says HTM teams must understand AI from the ground up, starting with how data flows through three key stages: collection and analysis, predictive modeling, and real-time application via learning algorithms.

But technical fluency is only part of the equation. As AI becomes more embedded in medical devices, Macht points to regulatory uncertainty, fueled by a shifting political landscape and a reported 20% cut in FDA staffing, as a growing concern. The agency’s current AI framework prioritizes transparency, patient-focused design, real-world performance monitoring, and life cycle oversight. But with the Trump administration back in office, Macht wonders how — or if — those priorities will evolve.

Academia, she argues, must fill the gap. “The academic sector can compensate for the disruption of the FDA’s processes by ensuring that the upcoming generation of [HTM professionals] have a curriculum that covers the foundational basics of AI including research ethics,” Macht says. “Regulators will also have knowledge gaps; and if the academic sector takes the lead in defining the core elements of foundational AI education for medicine and medical devices, both the agency and the industry will have a model to guide their program adjustments.”

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.

Finally, Hanna says AI can help take the guesswork out of end-of-life decisions. By running cost-benefit analyses, the technology can weigh whether a device is worth repairing, or if replacement makes more sense. The result? Smarter spending without sacrificing reliability. “These insights increase reliability, reduce costs, and improve planning,” he says.

The data dilemma
That’s not to say deploying AI is seamless, Hanna acknowledges, citing data quality as the top concern. Publicly available data sets, he notes, often “lack the specificity needed for precise device troubleshooting,” which limits the accuracy of AI-driven insights.

To address this, NVRT Labs is building curated knowledge bases tailored for AI. “We’ve incorporated long-term memory tools that retain key information across working sessions,” Hanna explains. That lets the AI pull relevant data on its own, solving problems in context, instead of starting from scratch each time.

Hanna cites data consistency as another major hurdle. Many devices use incompatible formats, limiting AI performance. And without clean, standardized data, even the best algorithms fall short.

Erin Sparnon, MEng, CSSBB, AAMIF, echoes that concern, particularly when it comes to data quality in computerized maintenance management systems (CMMS). “Incomplete or inaccurate data could lead to failure to derive insights or, worse, misleading conclusions that create a false sense of precision,” she says. “Either you may not get much in return for your investment, or you may waste time and resources chasing down insights that aren’t supported by your data.”

As a clinical engineering expert, Sparnon has seen firsthand how inconsistent naming conventions, missing serial numbers, and spotty repair histories can derail AI initiatives. Before layering in AI, she urges HTM teams to focus on data hygiene.

“I’ve participated in enough inventory normalization projects to wonder: Do you have an accurate inventory? Do your preventive maintenance and repair records tie directly to a complete asset entity — with key fields like manufacturer, model, and serial number filled out?” she muses.

One case particularly sticks with her: an entry labeled “Canon DR ED,” with no model or serial number in sight. Was it a boom-mounted digital radiography suite, or a mobile X-ray unit? There was no way to know, Sparnon says, rendering any AI-driven analysis meaningless.

She’s also encountered a dozen inconsistent spellings of “Lifepak,” along with conflicting strategies for updating manufacturer names post-acquisition. Consistency, Sparnon stresses, is key.

But even pristine internal data has limitations. Sparnon notes that HTM organizations must choose commercial partners whose AI models are trained on similar data sets, support local validation, and can normalize annotations or ticket types for meaningful benchmarking. High-quality data, she adds, isn’t just operationally critical, it’s a business asset. And without contracts that acknowledge its value in model development, organizations risk leaving revenue — and insights — on the table.

Sparnon also cautions against rushing to implement AI within existing CMMS platforms without first addressing foundational data quality. “The good news,” she says, “is that focused attention on data quality now will set you up for the next generation of insights, collaboration, and automation.”

Her advice to HTM teams evaluating AI? Start with a thorough review of inventory and documentation practices. AI is only as good as the data behind it — and that’s where AAMI EQ56 comes in. The updated standard outlines core elements of a strong equipment management program, from inventory control to quality oversight, giving HTM professionals a solid foundation for safe, data-driven decision-making.

And safety, Hanna reiterates, can’t be an afterthought. “Security should always be a priority,” he says. Proven tactics—data minimization, anonymization, encryption—still apply, just as they do for any software. But for sensitive operations, he advises going a step further: deploying edge processing or running systems locally to maintain control and protect patient data.

“AI systems must be transparent and reliable to support HTM professionals in their work,” Hanna says. Fortunately, new strategies are making AI not just smarter, but more trustworthy.

To boost reliability, NVRT Labs is turning to confidence scores; a system that ranks AI-generated outputs by certainty. When confidence dips below a set threshold, the task is flagged for human review. The result: AI handles routine duties, while HTM professionals take over when stakes are high, streamlining workflows without compromising safety.

“Internally, we validate performance with audit logs and usage tracking to help us refine prompts, tune behavior, and ensure alignment with safety and performance goals,” he says. That mix of transparency, control, and continuous oversight is what will drive AI’s responsible — and sustainable — growth in HTM, Hanna anticipates.

Looking ahead
Yes, the future is now, and HTM is arguably just beginning to tap into AI’s potential. Predictive maintenance, asset optimization, smarter training, and more efficient operations are on the horizon. But as experts like Macht, Sparnon, and Hanna emphasize, realizing that potential depends on a disciplined approach to ethics, data integrity, and cybersecurity.

For HTM professionals, moving forward means treating AI not as a silver bullet, but as a high-powered tool. One that demands sharper technical fluency, deeper regulatory insight, and long-range strategic thinking.

So will AI one day replace HTM professionals entirely? Sparnon is unconvinced. “AI has incredible potential to predict and alert for issues, even before they happen,” she says. “But until the algorithms can walk into a busy clinical setting and get equipment working again to support safe care, HTM teams will still be the first ones called when something needs attention — even if it hasn’t failed yet.”

In other words, AI may change the tools, but not the mission. And for HTM professionals, that mission remains the same: keeping technology safe, reliable, and ready when it matters most.