Unlocking the Value of AI in Medical Imaging

People worldwide are living longer. According to WHO, by 2050 the world’s population of people aged 60 years and older will double, reaching 2.1 billion. For healthcare systems, the growing as well as aging population translates into more patients with complex needs. A wide range of diseases such as cancer and cardiovascular conditions is on the rise. Accordingly, the demand for medical imaging is high – and increasing exponentially – to support screening, early diagnosis and subsequent monitoring of treatment outcomes.

Consequently, medical imaging data is growing and becoming ever more complex at a disproportionate rate when compared with the number of available trained readers. With megapixel upon megapixel of data packed into the results from scans, combing through more and more detailed images can be challenging even for the most experienced clinical professional. In fact, the amount of data is now so large that in some cases, radiologists would have to analyze one image every few seconds to manage their daily workload. While each image requires the radiologist's full concentration, errors may happen under time pressure. A publication from 2018 speaks of approximately 40 million diagnostic errors annually worldwide.

At the same time, fortunately, computational power has also grown exponentially, providing the perfect foundation for AI in medical imaging. These trends emphasize the leading role of radiologists, who have long been on the forefront of the digital era in medicine, in the introduction of AI into healthcare.

What’s in it for patients and their treating doctors?

Clinicians want to be able to reduce the time to treatment, find a way to identify and intervene early in disease states, reduce potential misses, and automate repetitive tasks. All of these play to the strengths of AI, augmenting the role of radiology experts to provide accurate and timely diagnosis for their patients. AI-enabled software can be utilized across a wide array of clinical conditions: for example, to help detect and prioritize potential stroke patients in an emergency setting, or to aid in early cancer detection. Quantification applications can help to automate tedious tasks for radiologists, such as lesion measurements.

As technology advances, and researchers continue to develop and refine applications, it will be increasingly important to join forces across disciplines. Close collaboration between radiologists, data scientists, software developers, experts in hospital IT and healthcare industries is needed in order to fully harness the opportunities AI provides.

However, with the development of AI algorithms for radiologists ramping up, the uptake in everyday clinical practice is still lacking. Moving forward, a key aspect for unlocking the power of this emerging technology will not only be to develop high-quality applications that radiologists can trust in. It will also be about facilitating their effective integration into daily clinical practice, so radiologists have a valuable aid at their fingertips and can concentrate on what really matters: providing an accurate and timely diagnosis for patients.