From Hardware to Intelligence: Charting the Course for AI in Medical Device Companies

Medical device companies face a unique challenge in the AI era: how do you transform organizations built around hardware engineering into entities capable of developing and deploying sophisticated AI solutions? Dr. Heather McCombs-Chait, with her deep experience in healthcare technology, offers a roadmap for this transition.

The Hardware-to-Software Shift

Traditional medical device companies have built their success on precision manufacturing, regulatory expertise, and clinical validation of physical products. The shift to AI-powered solutions requires fundamentally different capabilities: data science expertise, software development practices, and the ability to create products that improve over time through learning.

"The mindset shift is as important as the technical one," Dr. McCombs-Chait explains. "Hardware companies think in terms of fixed products. AI requires thinking about systems that continuously evolve."

Regulatory Navigation

One of the most complex aspects of AI in medical devices is regulatory compliance. The FDA has been developing frameworks for AI-based medical devices, but the landscape remains challenging. Companies must balance innovation speed with the rigorous validation requirements that patient safety demands.

Dr. McCombs-Chait emphasizes the importance of early regulatory engagement. Companies that treat regulatory strategy as an afterthought often find themselves with products that can't be brought to market. Those that engage with regulators from the start can shape their development processes to meet requirements while maintaining innovation velocity.

Building AI Capabilities

For traditional medical device companies, building internal AI capabilities requires strategic decisions about talent acquisition, partnership strategies, and organizational structure. Some companies choose to build dedicated AI teams; others partner with technology companies or acquire AI startups.

"There's no one right answer," Dr. McCombs-Chait notes. "The best approach depends on your existing capabilities, your strategic priorities, and the specific clinical applications you're targeting."

Clinical Validation Challenges

AI in medical devices faces unique validation challenges. Unlike traditional devices where performance is relatively static, AI systems may change as they encounter new data. This creates questions about how to validate systems that are designed to evolve.

Forward-thinking companies are developing new approaches to continuous validation that can accommodate AI's dynamic nature while maintaining the safety standards that healthcare demands.

Investing in the Future: Chicago's AI Rise and a Call for Responsible Innovation

Bringing the conversation home, we discussed the future of AI and Chicago's role in it. Dr. McCombs-Chait is optimistic about the city's vibrant ecosystem, which includes world-class academic medical centers, a thriving life sciences startup scene fostered by hubs like Matter, and the presence of innovative companies like Tempus. "I think Chicago gets a little bit overlooked as we think about both coasts, but it is a rich startup environment," she noted.

Looking ahead, she is most excited about the potential for AI to drive precision medicine, from designing better clinical trials to discovering new therapeutic uses for existing drugs. However, her excitement is tempered by a call for responsibility. "I hope we have the guardrails to continue to have ethical use of AI and we don't let it get out of control," she cautioned, stressing that AI should be used as an aid to augment human capabilities, not as a substitute for them.

Dr. McCombs-Chait's journey offers a powerful perspective for technology leaders, clinicians, and patients alike. It is a reminder that at the heart of technological transformation are people—the leaders who guide it, the teams who build it, and the patients whose lives it will ultimately impact.