With a growing demand for precision medicine, health systems are increasingly turning to digital innovation to enhance patient care.
To explore this shift, Becker’s Healthcare spoke with Christopher Garcia, MD, chief digital innovation officer at Mayo Clinic Laboratories, about how AI, big data and diagnostic transformation are converging.
Note: Responses have been edited for length and clarity.
Question: What does “data-driven diagnostics” mean to you?
Dr. Christopher Garcia:
Data-driven diagnostics is fundamentally about using computational tools to manage complexity that was previously impossible to handle. It encompasses three big categories. First would be using data and computation to move from being reactive to predictive, using patterns in large data sets to identify and stratify risk before symptoms appear. The second one would be moving from being siloed to integrated. For example, combining lab results with other modalities like imaging and clinical notes and genomics. The third is moving from population averages to precision, really getting that personalized baseline and trajectory for each patient as opposed to large population benchmarks.
Q: What’s changed the most in diagnostics over the past few years?
CG:
The development and growth of foundation models and multimodal AI has been huge. I have worked a lot with AI in my career and have trained and worked with folks in developing solutions in the lab. The big move from small narrow AI models to providing solutions to lots of different problems with the same tools is incredible. Not only are there new tools, but cloud computing and improved algorithms mean that it’s more available for everyone than it was before.
Q: Looking ahead, what excites you most about where diagnostics is going?
CG:
I’m particularly excited about democratizing expertise. Using AI to deliver subspecialist-level insights to underserved communities could really help close equity gaps in healthcare. I’m also intrigued by the concept of continuous learning systems, diagnostics that improve every time they’re used.
Q: With so much excitement around AI, how do you strike the right balance with clinical adoption and patient safety?
CG:
Patient safety is paramount. Not only is it important to take care of the patients, but we have to sustain the infrastructure, the talent, the expertise and everything that it takes to be able to create these tools. As we work in this space and work with others, these tools or these methods need to prove their value.
Q: What do you hope to be true about diagnostics five years from now?
CG:
I hope AI becomes recognized as a necessity, not just a curiosity in diagnostics. I want it to be embedded in how we deliver care. I also hope we’ve made meaningful progress in bringing high-quality diagnostic capabilities to everyone, not just those in resource-rich areas. Finally, I want to see technology well-integrated into teams and workflows. It’s about making better decisions with a clear understanding of each tool’s limitations and strengths.
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