AI as a Second Set of Eyes

Our Europe MedTech Analyst digs into the transformational impact of AI-driven diagnostic imaging on healthcare systems.

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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.

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