Envisioning the AI-Empowered Patient and Physician of the Future

Assume that the changes outlined so far march forward, driven by advances in technology and wider adoption. Misconceptions about AI will also continue to erode, and successful deployment of AI-assisted technology may increase public trust in algorithmic living. Who can guess how fleets of autonomous vehicles smoothly navigating city streets and highways will change perception?

AI also notoriously employs a so-called “black box”—the essential genius of deep-learning unfolds behind a closed curtain. We know the input and output, of course, but seldom the step-by-step process of the AI reaching its conclusion. It can be difficult to trust an answer when the underlying work is hidden (and may contain accidental bias).

Trust will likely grow over the next decade, but even without that embrace, things may wildly transform—as much as possible in the regulated world of medicine.

Genomic sequencing is, in itself, a revolution. Mapping a genome opens a window into inherited conditions, susceptibility to disease, potential response to treatment, and countless other insights. But the process takes time and the considerable might of high-performance computers.

Lenovo recently set out to look at pushing sequencing to population levels—dropping 150 hours of computational time to something much more manageable. One proposed solution, which more readily scales than most, clocks in at just 5.5 hours for a full genome. Imagine that figure after another 10 years of innovation.

Barcelona supercomputer MareNostrum
The MareNostrum supercomputer–housed in a Barcelona church–where Lenovo helps drive powerful genomic research.

“What if we could sequence everyone and develop truly personalized medicine?” Tease asked. “If AI and HPC drive the computational time down so low that on-demand, virtually anyone could get that diagnostic edge and identify the ideal, individualized treatment.”

Even beyond that kind of massive rollout, Tease imagines a world where AI enhances access to quality care.

“A patient anywhere in the world could share a blood sample and deep biometrics,” he said. “AI steps in for the initial analysis, finding patterns, diagnosing the patient, and recommending next steps. Then, as necessary, the case escalates to the right expert, who now has all that information at their fingertips.”

Rendering of a DNA double helix

Add in the possibility to assist and treat with augmented reality tools, and both efficiency and quality soar while costs plummet. How that plays out in the regulatory space will be interesting and potentially painful—though early adoption gives us reason to be optimistic.

Making the future even brighter, consider that AI algorithms are only as powerful as the data available. That essential data upon which machine-learning thrives will push big data analytics in healthcare to nearly $70 billion by 2025. Who can predict how the AI of 2030 will empower both patients and providers?

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