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Welcome to Thoughts on the Market. I'm Robert Davies, Morgan Stanley's head of the European Medtech research team. Today I wanted 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 5th at 10:00am 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 US 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 three to four seconds during an eight hour workday. This is the current pace needed to meet soaring demand. At the same time, the US population is getting older and a growing number of people are signing up for Medicare. Healthcare costs are continually rising. Total US healthcare spend is now hitting 4.5 trillion. That's nearly 20% of total US GDP. On top of that, patients need fast, accurate diagnosis. But long wait times often mean 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 the hospital system each year in terms of diagnostic imaging. Smart imaging uses AI tools to improve image 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 diagnosis. 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 and pattern recognition. That's crucial in reducing diagnostic errors, a problem costing the US healthcare system an estimated $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's only able to spend three to four 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 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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