The healthcare artificial intelligence (AI) market was worth around $11 billion worldwide in 2021 and is forecasted to grow up to $188 billion by 2030—increasing at a compound annual growth rate of 37 percent. AI in medical imaging, a large segment of this market, was valued at $1.7 billion in 2022 and is projected to reach $20.9 billion by 2030. Put simply, the significant opportunity for AI in medical imaging is on par with that of AI in healthcare overall.
Medical imaging refers to the use of technology to view the human body in order to diagnose, monitor, or treat medical conditions; technology used to generate medical images includes magnetic resonance imaging (MRI) scans, ultrasounds, X-Rays, Computed Technology (CT), and other tools.
A wave of new AI/machine learning (ML)-enabled medical devices has come to market in recent years, with an 83% increase in approvals by the Food and Drug Administration (FDA) seen in 2021 compared to 2018.
To stay ahead of the curve and maximize market opportunity, MedTech companies must prioritize innovative data solutions and end-user optimization that supports the development of MedTech AI.
Here’s a look at the current state of medical imaging AI and where the industry is trending, as well as some regulatory risks and implementation/adoption challenges within the space that can impact the application of this technology.
More than 500 AI-enabled medical devices have received FDA approval and are available in the market today. These devices span a wide range of functions, but there are some use cases that are largely applicable to the medical imaging:
AI models are being used to augment and deliver higher resolution images—and potentially even enhance captures such as 3D models for easier diagnosis.
Medical diagnostics are being streamlined—and accuracy improved—through the integration of AI-enabled medical devices.
AI can provide a more efficient approach to speech recognition software that minimizes redundancies for radiologists when dictating findings for reports. AI-enabled technology is providing efficiencies beyond speech recognition through automatic prioritization of cases based on need and radiologist availability, the optimization of workload imbalances across radiology departments, and real-time clinical intelligence and decision support.
AI continues to revolutionize the medical imaging landscape, and integration with Augmented Reality (AR) is emerging as a notable trend. AR, in conjunction with AI, facilitates the creation of immersive 3D models from medical images, thus enhancing a surgeon's understanding and manipulation of complex anatomical structures. A pioneering example of this is the Orsi Academy's application of NVIDIA Holoscan in the execution of in-human robot-assisted kidney surgery. This innovative approach is anticipated to become increasingly instrumental in advancing surgical precision and patient outcomes.
Another transformative trend in medical imaging is the convergence of AI, Precision Medicine, and Radiomics—the extraction of many quantitative descriptors from medical images. The integration of these three spheres offers significant potential to improve imaging interpretation, particularly in the context of complex pathologies such as brain tumors. AI algorithms can mimic neural networks, thereby facilitating tumor genotyping, the precise delineation of tumor volume, and the prediction of clinical outcomes.
An emerging field known as Radiogenomics seeks to link genomic features with imaging biomarkers of a given disease, thus personalizing patient treatment strategies. This integration also aids in early risk identification, such as pre-metastatic niche detection and is pivotal in the development of Theranostics, an approach that combines specific targeted therapy based on specific targeted diagnostic tests.
The role of AI in medical imaging is becoming increasingly significant in the context of emerging infectious diseases. With novel infectious diseases posing continual global health challenges, the need for rapid and accurate diagnostic tools is paramount.
AI can assist in automating the identification and interpretation of imaging biomarkers related to these diseases, thereby accelerating diagnosis, optimizing treatment, and potentially curbing disease spread. As research and development in this area continue, AI's role in infectious disease imaging is expected to grow, contributing to improved global health security.
While applications of AI in medical imaging have the potential to greatly improve the diagnosis and treatment of diseases—especially in oncology—there are several factors posing challenges to the development and adoption of these technologies.
Healthcare companies can prepare for the new regulatory standards of premarket assurance, pre-specifications, GMLP, and ongoing monitoring in the following ways:
Improving user experience (UX) and adoption for AI applications in medical imaging involves a multi-faceted approach that emphasizes usability, transparency, and meaningful interactions. Some key principles of effective UX include:
By focusing on these aspects, AI solution developers can create applications that not only deliver powerful AI capabilities but are also easy to use and well-integrated into the healthcare environment, thereby improving user experience and adoption.
Lastly, companies ought to perform cost/benefit analysis in order to address the conundrum of in-house vs. cloud for AI applications and choose the best tech stack for their use cases.
Doing so means performing a detailed total cost of ownership (TCO) analysis for in-house and cloud-based AI infrastructure. To account for costs, companies should not only measure the direct costs of hardware or cloud services but also indirect costs (such as maintenance, upgrades, internal computing costs, and security measures).
Regarding benefits, it’s necessary to first define strategic objectives and then consider how factors like scalability, flexibility, and potential improvements in AI performance and capabilities are optimized under the buy vs. build structure. In many cases, firms have outsourced infrastructure and platform layers cloud service providers due to superior back-end technology. Examples include healthcare AI innovators such the Mayo Clinic and Zebra Medical Vision have built their AI/ML platforms on Google’s Cloud tech stack.
The wave of innovations in MedTech AI, especially in medical imaging, is showing us the power of AI-enabled technologies in healthcare. At the same time, these advancements are highlighting technical, business, and regulatory challenges that must be considered and addressed for these technologies to have their full intended impact and continue to grow in sophistication and application over time.
To meet these challenges, MedTech companies and healthcare provider organizations alike need to focus on their alignment to evolving regulatory principles, end-user experience, and IT infrastructure. Ongoing attention to these three main areas is imperative for the continued advancement of the healthcare industry’s relationship with AI.