Matching Machine Learning and Medical Imaging: Predictions for 2019

Developments in vastly scalable IT infrastructure will soon increase the rate at which machine learning systems gain the capacity to transform the field of medical imaging across clinical, operational and business domains. Moreover, if the pace seems to be picking up, that’s because data management on a massive scale has advanced exponentially over just the past several years. 

Consider: Current graphics-processing unit (GPU) technology uses several thousand processing cores and delivers more than 100 teraflops of deep learning performance. GPUs are particularly well suited to image processing, which is a perfect first fit within healthcare for convolutional neural networks (CNNs). That’s why CNNs are increasingly being applied to many medical imaging applications, most conspicuously (and least surprisingly) in radiology.

From a global view, it’s clear that the acceleration of performance improvement in medical imaging AI systems—specifically as supported by advances in data storage and other aspects of IT infrastructure—is just one of numerous game-changing scenarios likely to unfold in the weeks, months and years just ahead. 

All are worth considering in some detail. Possibly none would be considered especially noteworthy outside of technology circles if not for one salient point: The medical imaging industry is still in a very early phase of its adoption of AI-fueled innovation. Whatever specific applications and new wrinkles emerge next will disrupt all aspects of the profession. That’s why now is the time for healthcare providers to begin constructing a machine learning strategy—and to leave room in it for adjustments to unforeseeable developments as well as expected end points.  

Before charting a path to there from here, it will be helpful to consider some of the aforementioned near-future scenarios and what to expect as each unfolds. 

• The market for machine learning is surging, fueled by a need to improve accuracy and efficiency. So far, the use of machine learning in routine diagnostic imaging has been limited. That is poised to change dramatically in the coming years as CNNs and other deep learning techniques proliferate. Forces driving the market for AI-based software that analyzes medical images include increasing radiologist workloads, errors and discrepancies in radiologists’ image interpretations, and the increasing use of AI for nonclinical applications such as workflow prioritization, quality assurance and overall practice management.


• Machine learning will improve the care experience for radiology patients. From initial scheduling of the imaging examination to final diagnosis and follow-up appointments—and every step along the way, including image acquisition, findings reporting and treatment planning—AI and machine learning will facilitate smarter, more streamlined processes that help achieve better outcomes while also adding to overall patient satisfaction. For providers this will translate to operational efficiencies and substantial cost savings.


• Research and innovation will yield new and compelling use cases. As of now the radiological subspecialty furthest along with AI is screening and diagnostic breast care. This is so because the field has been using pre-AI computer-assisted detection (CAD) techniques to find cancers for many years, and machine learning has been a natural next step. However, use cases for advanced AI-aided image analysis are well in development for diseases of the lungs, brain, heart, musculoskeletal structures and all internal organs.


• Technology vendors will offer new medical imaging AI products as quickly as academic research centers can test and prove new algorithms. Studies on machine learning have been expanding rapidly in recent years in peer-reviewed medical journals. Accordingly, established technology vendors are collaborating with the researchers at a heightened pace, seeking to remain competitive in an AI-driven healthcare market. To that end, strategic distribution and technology licensing partnerships are increasing and the first online AI marketplaces are already open for business.