How machine-learning technologies can help clinicians and radiologists alike
With the RSNA 2017 Annual Meeting fast-approaching, topics such as artificial intelligence and other image processing are expected to be top of the agenda. Indeed, RSNA is dedicating a specific area of the show to these kinds of technologies in the Machine Learning Showcase, located in North Hall B.
There is a lot of buzz around this subject area at the moment, and extensive investment, research and development is being dedicated to exploring how machine learning can help deliver improvements in a wide range of healthcare areas.
Our experiences in the radiology market made it clear that there had to be a better way for these new imaging technologies to be adopted and deployed. We realized that by adopting a platform-based approach, we could streamline the technology adoption process for other solutions.
In this blog, we provide a preview of three broad areas where intelligent technology can help radiologists and referring physicians:
- Condition-Specific Applications
- Efficiency Applications
- Planning Applications
The ability to quickly access condition-specific information before exams are read can be of great assistance to radiologists and clinicians alike. Machine learning applications can automatically generate this information, so all relevant information is already available, and staff can focus on adding clinical value.
Such applications can help reduce the cost of care and improve diagnostic confidence by making reading more efficient and providing informed reporting to optimize treatment planning and help improve outcomes.
As we move to value-based care, providing actionable value to the referring physician is key. With radiology workloads at an all-time high, new applications in this field often seek to reduce the time radiologists spend performing routine tasks. Applications leveraging AI and intelligent image processing can enable automated detection of specific findings to aid radiologists and reduce time spent on examinations, so they can spend time on more rewarding, higher-value tasks.
A successful efficiency application will both automate workflow and standardise results, and should ideally be relevant across a broad spectrum of body parts and conditions. Pre-processing can help automatically extract relevant information from images and deliver outcomes back to PACS or other image viewers for use when images are read.
This area of machine-learning development is focussed on helping surgeons, oncologists, orthopods and other specialists prepare and communicate interventional procedures and treatment plans. These kinds of applications deliver improved efficiencies, help advance surgical standards and improve patient outcomes.
Despite their clear benefits, adding multiple AI and intelligent processing applications can be expensive and time-consuming. Which is why, at Blackford, we partner with leading technology providers to ensure multiple automated processing solutions can be quickly deployed on a single platform.
Already fully integrated into existing imaging systems, our platform hosts multiple AI and intelligent image processing modules and controls the flow of medical imaging data to and from applications.
By automating workflows and providing standardized results, a platform approach provides fast, unified access to solutions that help healthcare professionals improve diagnostic confidence and patient outcomes. Ultimately, we reduce the cost of care and increase the ability to add clinical value by increasing efficiency and productivity.
Please contact Blackford Analysis to confirm medical device regulatory status of specific applications in your region.
Contact us to find out more on how the Blackford Platform can be used to deploy multiple AI, machine-learning and intelligent image process technologies.
Or click below to download our eBook and discover how a platform approach allows you to deploy AI and medical imaging applications quickly and easily: