Computer scientist Frank Rudzicz researches the uses of machine learning in natural language processing and speech recognition to improve the quality of life for individuals with cognitive or physical disabilities.
Computer scientist Frank Rudzicz, PhD, researches the uses of machine learning in natural language processing and speech recognition to improve the quality of life for individuals with cognitive or physical disabilities. We caught up with him to learn more about where he sees the field of AI in medicine heading in the future.
What inspired your interest in AI in healthcare?
I was interested in AI when I was a kid, but its intersection with healthcare was crystallized during my PhD research to augment speech recognition’s acoustic information with a model of how the vocal tract physically produces speech. I had been thinking like a computer scientist, only in terms of equations and code, but during some data collection for the project, I talked to people with cerebral palsy, who were crucial to the project, and they told me of their deep frustrations with current technology. Suddenly the goal wasn’t just papers and a thesis, but actually producing something in the real world to help people.
What's the biggest challenge you face in your work?
One of the biggest challenges remains managing expectations. At first, the field suffered from too much hype and now it’s suffering from too much cynicism and doubt. It’s important to focus not just on what can be done, but what has been done, and to describe that work in terms that matter to the intended user.
What advice would you give to computer science or medical students who would like to follow in your footsteps?
It’s crucial for everyone to find their own path. That said, I think there are some general principles. There is a philosophy called ‘Ikigai’ that says to find your path you should consider each of what you’re good at, what you love, what the world needs, and what they’ll pay you for. It’s also important to maintain a balance—not just between life and work but, within your work, between depth and breadth; become an expert in your niche, but also make sure you acquire a wide range of tools that you can use as new opportunities emerge. And finally, always remain open to new experiences and new perspectives.
What do you like to do when you aren’t working?
Outside of work, spending time in nature with my family is the best. I also enjoy cycling and reading, but not at the same time.
Are there any projects or initiatives you’re working on that you’re really excited about?
I am very excited about what we’re doing in surgical safety. The OR Black Box™ is a flexible collection of technologies that can be similarly redistributed to a variety of applications and one aspect of it is multiplicity. For instance, we can use computer vision to identify adverse events using a camera inside a patient during surgery. But if we combine that with an external view of the room and microphones in the room, we can track distractions, identify the causes of the event, and prescribe change.
What do you find most interesting about the future of AI in healthcare?
There are so many interesting and important avenues such as privacy, explainability, trust, and bias. One area that is less often discussed is human-computer interaction and design. We often hear about AI replacing doctors and nurses but, actually, the goal is augmenting doctors and nurses with new tools. It will be important to design tools to be usable. We also need to build clear and consistent communication pathways between healthcare practitioners and AI software so that we can avoid various risks and harness all of the various benefits.
