Our Europe MedTech Analyst digs into the transformational impact
of AI-driven diagnostic imaging on healthcare systems.
----- Transcript -----
Welcome to Thoughts on the Market, I’m Robert Davies, Morgan
Stanley’s Head of the Europe MedTech research team. Today I want
to take you behind the scenes to show you how AI is
revolutionizing our approach to medical diagnostics via Smart
Imaging.
It’s Thursday, December 5, at 10 AM in Boston.
When was the last time you needed to get an X-Ray, a CT scan, or
an ultrasound? Depending on where you live, your wait time could
be as long as a month.
Medical diagnostics through imaging is facing enormous challenges
right now. Population growth, rising longevity, and intensifying
chronic disease burdens are driving ever increasing volumes of
medical scans. In the U.S. alone, CT scan volumes have quadrupled
since 1995. So, what is the impact of this? Imagine a radiologist
interpreting a CT or MRI image every 3-4 seconds during an
eight-hour workday. This is the current pace needed to meet the
soaring demand.
At the same time, the U.S. population is getting older and a
growing number of people are signing up for Medicare. Healthcare
costs are continually rising, total U.S. healthcare spend is
now hitting $4.5 trillion. That's nearly 20% of U.S. GDP. On
top of that, patients need fast, accurate diagnosis. But long
wait times often mean that patients don’t get the diagnostic done
in time or sometimes not at all. All of this indicates that more
and more stress is being placed on hospital systems each year in
terms of diagnostic imaging.
Smart Imaging uses AI tools to improve imaging processing and
workflows to enhance traditional image gathering, processing, and
analysis. It sits at the intersection of Longevity and Tech
Diffusion, two of Morgan Stanley Research’s big themes for 2024.
And it can help solve these acute demand challenges. In fact, AI
is already transforming the $45 billion Diagnostic Imaging
market.
AI-driven Smart Imaging integrates into the diagnostic imaging
workflow at multiple stages—from preparation and planning, all
the way to image processing and interpretation. The primary
benefits of using AI are twofold. Firstly, it enhances image
quality, which ensures more accurate diagnoses. And secondly it
improves the speed, efficiency, and overall comfort of the
patient journey. At the same time, AI effectively acts as a
second set of eyes for the radiologist, often surpassing human
accuracy in pattern recognition. That's crucial in reducing
diagnostic errors—a problem costing the U.S. healthcare system
around $100 billion annually at the moment.
In addition to minimizing misdiagnosis, AI is not only capable of
identifying the primary disease, but also registering any
potential secondary diseases. Otherwise, this isn’t normally a
priority for the radiologist who is only able to spend 3-4
seconds looking at any individual image. But it’s a potentially
life-saving benefit for using Smart Imaging applications.
So how does AI fit into the clinical setting? There are multiple
stages to the Diagnostic Imaging workflow and AI can play a role
across the entire value chain from preparing a patient’s scan, to
processing the images, and finally, aiding in the diagnosis,
reporting, and treatment planning.
Radiology is currently dominating the FDA list of AI/Machine
Learning-Enabled Medical Devices. And when we look at the broader
economic implications, it's clear Smart Imaging represents a
pivotal development in healthcare technology that has broad
implications for healthcare costs, quality of care, and better
healthcare outcomes.
Thanks for listening. If you enjoy the show, please leave us a
review wherever you listen and share Thoughts on the Market with
a friend or colleague today.
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