One thing was made crystal clear on the heels of the Radiological Society of North America’s (RSNA) annual conference. Artificial intelligence in radiology is a big thing. Compared to the prior year, the number of machine learning companies presenting their products and services more than doubled. Likewise, in recent years, more than 100 startups have entered the AI medical imaging space, including neuroradiology AI. However, the use of artificial intelligence in radiology doesn’t mean radiologists will soon be out of work. As a matter of fact, it means just the opposite. In the process, neuroradiology AI and other services will significantly enhance medical care for us all.
What Artificial Intelligence in Radiology Looks Like
As with any technological disruption, the use of artificial intelligence in radiology might be a bit unnerving if you’re a radiologist. It, however, is not the case in neuroradiology AI, chest radiology AI, or other similar systems. Using a people-centric approach, businesses are developing artificial intelligence in radiology by recruiting radiologists to facilitate development. In essence, these systems enable radiologists to collect image data, validate findings, and actually train AI systems. In this way, radiologists are at the center of advancing deep learning algorithms for artificial intelligence in radiology software systems.
From a practical perspective, what does this mean? The obvious application of this artificial intelligence in radiology systems will be as decision support structures for radiologists. As these systems improve in their deep learning capacities, they can provide screening detection and oversight for radiologist interpretations.
For example, a neuroradiology AI platform might screen head CT scans for emergent stroke evaluations before formal radiologist evaluations. This artificial intelligence in radiology systems are not this advanced yet, but the future looks very bright in this regard.
Overcoming Challenges in Neuroradiology AI and Other Areas
A number of challenges exist when it comes to using artificial intelligence in radiology. For one, radiologists do much more than to simply interpret a single image. A single radiology image requires dozens of critical assessments and considerations before making a final interpretation. These processes are even more complex for some imaging types like brain MRIs. The capacity of neuroradiology AI platforms to accurately manage these tasks is well beyond the scope of current systems.
Training neurology AI systems and other radiology machine learning platforms are therefore a major challenge for businesses today. Likewise, the use of artificial intelligence in radiology requires system validation, data preparation, and incorporation into normal workflows.
For these reasons, bold businesses are developing artificial intelligence in radiology to augment imaging interpretation quality and efficiency. In doing so, neuroradiology AI platforms and others can progressively improve while boosting existing healthcare services.
Emerging Artificial Intelligence in Radiology Leaders
As you might imagine, businesses interested in providing artificial intelligence in radiology services are growing. Incredible advances are being made as evidenced by vendors in Chicago at this year’s RSNA meetings.
The following businesses represent some of the most well recognized artificial intelligence in radiology platforms currently.
- Philips IntelliSpace Discover 3.0 – This advancing AI platform is currently being used in over 50 hospitals and academic institutions. Though used primarily in clinical research, the system provides radiologists with data analytics where further AI training and deep learning can occur. Likewise, the Philips system anonymizes patient data thus complying with patient privacy requirements.
- GE Healthcare Edison Platform – GE Healthcare also recently introduced its artificial intelligence radiology platform as well. Through dozens of clinical and business partners, GE Healthcare plans to connect millions of medical images. This will allow deep learning algorithms to advance, and these applications can be utilized on all kinds of smart devices. GE is already evaluating the ability for artificial intelligence in radiology to detect life-threatening pneumothorax cases on chest CT.
- Nuance PowerScribe 360 Clinical Guidance – Nuance is a recognized leader in healthcare technologies, and it also has entered the medical imaging arena. Nuance’s PowerScribe 360 offers open access, image sharing platform where artificial intelligence in radiology can be advanced. Nuance utilizes this and cloud technologies to gain extensive inputs about images from numerous radiology experts. As a result, its AI platform is expected to make huge impacts on neuroradiology AI and other radiology AI areas.
The Promise of Artificial Intelligence in Radiology
Though neuroradiology AI systems and other platforms are still evolving, the potential for change is notable. Presently, these systems are being employed in clinical research areas where further deep learning algorithms can be developed. But experts anticipate clinical use to aid radiology productivity, and efficiency will soon emerge.
These advantages in productivity will be what initially drives the economics of neuroradiology AI and other platforms.
However, in time, productivity gains will give way to improvements in healthcare quality. Artificial intelligence in radiology has a tremendous potential to utilize data for more accurate and timely diagnoses. Similarly, these systems offer opportunities for advancing public and population health for all societies. Ultimately, this means better use of resources in healthcare and better quality of human life. These are the long-term promises for artificial intelligence in radiology, and bold businesses see these opportunities today.
