MedTech Leaders Warn AI Adoption Is Complex

Artificial intelligence in medtech was a dominant topic at AdvaMed’s The MedTech Conference last week, with multiple panels and keynotes examining regulatory outlooks and practical applications.

Industry leaders warn of deployment complexity

One speaker noted that the technology is more complex than many anticipate. “My hot take is that it is more complicated than people think it will be,” the executive said, adding that actual deployments are far fewer than public perception suggests.

The speaker highlighted the regulatory burden of scaling AI agents across quality-sensitive processes. “It’s going to be very, very challenging for folks to do this well,” they warned, noting that managing a handful of regulated IT systems already strains resources.

Estimating hundreds of potential use cases per company, the executive noted that roughly half may reside in research and development. “How do you actually deploy all that and design it?

Company-specific AI strategies

ResMed’s chief executive outlined a vertical-focused approach, targeting sleep and breathing health as the firm’s core market. “Own your vertical. Create the infrastructure,” the leader asserted, positioning the company as the global leader in home-based respiratory care.

According to the executive, AI already supports supply-chain efficiencies that offset inflationary pressures. “We use AI in supply chain. We’re using it to create really great cost savings,” they said, pointing to the company’s extensive data set of 28 billion nights from its MyAir and AirView platforms.

A cybersecurity and policy expert warned that expanding AI authority without clear limits can expose patient data. “An AI system can produce an accurate answer and still share patient information improperly,” the speaker noted, linking policy definition with security controls.

The expert stressed the need for real-time validation of each AI action, arguing that oversight must precede execution to prevent regulatory breaches.

Consulting perspective on productive use

Mike White, a partner at Alexander Group, differentiated between productive and unproductive AI applications. “There will be a wide difference in productive versus unproductive uses of AI, and then we’ll learn in the coming six months, a year, two years,” he said.

White cited examples where AI enhances business functions, such as automating inbound lead qualification to boost sales efficiency.

He also expressed skepticism toward AI that attempts to read facial expressions during video calls, suggesting that sales reps should rely on their own judgment. “My perspective is the rep should be able to just know that without AI,” he concluded.

While the industry pushes forward, the pace at which firms can safely integrate AI will likely dictate competitive advantage. Companies that balance innovation with robust governance may avoid the pitfalls highlighted by multiple speakers.