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.”

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