Ultrasound imaging is one of the most used diagnostic procedures in the healthcare field.^[1]The technology plays a critical role in timely diagnosis, disease management and treatment planning across many medical specialties, from pediatrics to women’s health.

Advances in ultrasound technology such as embedded AI and deep learning algorithms can significantly assist providers and the patients who rely on them for the best possible care. Clinicians can make confident diagnoses faster as equipment becomes more innovative, meaning the patients they treat get the right interventions at the right time.

Strategically positioning ultrasound as a valuable diagnostic tool in a provider’s digital ecosystem of connected medical devices can take ultrasound to the next level with access to analytics and productivity solutions.

“As we think about the pressing problems of increased demands, the need for more efficient productivity and the goal to improve health outcomes exists not only in ultrasound, but across the global healthcare spectrum,” explains Karly Yoder, General Manager and Chief Digital Officer for GE Healthcare Ultrasound. “At GE Healthcare, we think it requires a strategy and a focus around precision health. It means we want to do the right thing at the right time for every patient at a global scale. That is a big vision, but we know it's the right vision if we want to move healthcare into the future that it requires to address these pressing issues.”

Ultrasound is used across nearly every clinical area in healthcare. Industry leaders such as GE Healthcare are innovating ultrasound with improved usability and flexibility, as well as improvements in ultrasound’s image quality and clinical decision support tools. Thanks to important artificial intelligence (AI) tools, clinical applications such as breast ultrasound and pediatric interventional radiology are seeing some impressive results.

AI tools, connectivity, and innovation in ultrasound

Interest in AI in healthcare is on the rise, with $4 billion invested into the sector in 2019, up from $2.7 billion in 2018.^[2] Many AI-based solutions have already been adopted into healthcare environments to support specific processes and tasks. There are also many other new AI solutions and machine learning algorithms available, but the adoption rates don’t reflect the rates of development.

“The problem is when we actually look at adoption of AI for some of these digital technologies, we see a rate of about 10 to 15 percent of adoption,” explains Yoder. “Why is that? We as a healthcare industry need to be obsessed and focused on how we make this adoption simple, basically invisible, into the workflows that healthcare providers already use and trust across their departments, and across their institutions.”

Clinicians are less likely to be enthusiastic about AI solutions if they cause them more administrative work in their workflow. To gain physicians’ trust, according to Harvard Business Review, AI-software developers will have to clearly demonstrate that when the solutions are integrated into the clinical decision-making process, they help the clinical team do a better job. The tools must also be simple and easy to use.^[3]

“GE Healthcare is approaching AI development and implementation from two perspectives,” says Yoder. “The first ‘mile’ is all about building applications that matter to customers and driving operational as well as clinical outcomes in the right direction. Getting this right means leveraging AI and the building blocks of a healthcare ecosystem. The last ‘mile’ is about addressing the adoption challenge. So, if you’ve built a wonderful solution that does incredible things in the healthcare setting,” she explains, “but you’ve developed it in a way that requires a clinician to go to a new computer, open a new application, or get on the phone and have to call somebody, you’re not going to see adoption because it breaks the workflow.”

Ultrasound AI innovation from GE Healthcare is not only embedded on the systems themselves, but also available on GE Healthcare’s Edison™ platform. This platform delivers AI and digital solutions developed by GE Healthcare, as well as third-party developers. These solutions are easily implemented into clinicians’ existing workflows and can allow experts to get the right diagnosis quickly for their patients, but also allows novices to act like experts.

“And these are the type of transformational changes we can make when we embed AI tools deeply within our products,” she adds.

How AI is changing the game in breast ultrasound

To aid clinicians in finding breast cancers, AI-based tools have been introduced and show great promise in increasing diagnostic accuracy, as well as creating substantial improvements in productivity.^[4]

An AI-based quantitative risk assessment tool, Breast Assistant, powered by Koios DS™, provides clinical decision support for physicians and technologists when using ultrasound to detect and diagnose breast cancer. It’s easy to use and results have shown higher rates of diagnosis, and fewer biopsies.^[5]

“We've got issues of missed cancer. We've got issues of avoidable biopsies. We've got issues of unnecessary follow-up and a dramatic amount of variability,” explains R. Chad McClennan, CEO of Koios. “Now there's a new solution. There's diagnostic AI.”

He explains their AI solution is not like traditional CAD. It's not the binary choice between normal or abnormal. It's the interpretation of something that's abnormal. Providing clinicians with an automatic BI-RADS® assessment, and an accurate interpretation is resulting in a positive impact to patient outcomes, so that there's no need for the patient to stress or undergo a subsequent procedure in some cases.

Improving outcomes using point of care ultrasound in pediatric IR

In other clinical areas such as interventional radiology (IR) and urgent care, ultrasound is well established as a safe and effective imaging modality for the rapid diagnosis and management of many emergency conditions. At the bedside, it also improves success and patient safety during invasive procedures.^[6] With many technological and digital advances as well as the increasing availability of imaging technologies, there has been considerable expansion of the use of clinical ultrasound—including both radiology-performed consultative studies and point-of-care ultrasound (POCUS) studies.^[7]

Jay Shah, MD, pediatric interventional radiologist (IR) at Children's Healthcare of Atlanta and Assistant Professor in the Emory University School of Medicine Department of Radiology and Department of Pediatrics in Atlanta, Georgia, covers two hospitals with a group of four IRs. They are tertiary care hospitals with a collective 800 beds. Two hundred of those are ICU beds, including NICU, PICU, TICU and any other acute care.

Ultrasound is a mainstay for Dr. Shah and his team and in addition to its clinical value, it helps them reassure their patients’ parents, most of whom are a little nervous, or a little anxious because they’ve usually seen several other providers before landing in pediatric IR. Due to the types of clinical issues they treat, they often use ultrasound for biopsies, aspiration and drainages, but because it involves no ionizing radiation, it can be utilized more.

“Ultrasound is our workhorse,” notes Dr. Shah. “It’s real-time. It’s accurate, and it gives off no ionizing radiation. Additionally, it’s widely available. And in [pediatrics] we try to use it as strategically as we can. We successfully utilize it as a therapeutic procedure whenever it is available. In patients with repeated procedures, it offers no radiation exposure when we do follow ups, and we can monitor lesions that otherwise may or may not require retreatment.”

The team relies on ultrasound for procedures such as tumor ablation where they can use central venous access at the bedside or in emergent situations. They also perform thoracic interventions and vascular malformations, as well as use advanced techniques such as contrast-enhanced ultrasound.

“For us in clinic, we use it just like every other clinician does. We take a thorough history, and we do a good physical,” explains Dr. Shah. “But the other thing we get to add is what I like to call the radiology physical, which is point of care ultrasound. It’s so crucial for us and it helps our parents leave with a definitive plan. We can sometimes give them a preliminary diagnosis, but even if we can’t, we can give them some sort of idea as to what’s going to come next and whether or not the patient’s going to need further imaging or a procedure.”

Driving a smarter, more efficient future with ultrasound

Ultrasound technology and innovation is evolving to meet the needs of the changing healthcare landscape. The need for more efficient solutions is driving ultrasound providers in the industry to create smarter solutions. AI and deep learning technologies provide clinical decision support, save time, and importantly, create solutions that are easy to use and implement across health systems. GE Healthcare continues to support clinicians with game changing solutions in ultrasound such as powerful portable ultrasound for point of care use, and deeply embedded AI tools for accurate breast cancer detection.

RELATED CONTENT

Harnessing the power of AI: decision support in breast diagnostics
- BI-RADS® helped standardize the classification of breast lesions in ultrasound. Still, clinicians interpret cases differently. Learn how AI can help reduce variability in BI-RADS categorization to achieve greater consistency and confidence in the decision-making process.
- View replay now
POCUS: Evaluation and Procedure for the Pediatric IR
- POCUS ultrasound has become an essential evaluation tool of specialties other than Radiology. Portable ultrasound is an impactful supplement to the physical exam, expediting the course of a patient's medical journey. While still considered healthcare's imaging experts, Radiologists and Interventional Radiologists must be comfortable with POCUS to remain relevant amongst today's healthcare environment. Learn how POCUS can be integral to the work-flow of a large academic pediatric interventional radiology service.
- View replay now
Ultrasound. Digital ecosystem
- Hear from Karley Yoder, General Manager & Chief Digital Officer, Ultrasound, GE Healthcare, who shares the vision of AI development and implementation in health systems and for ultrasound.
- View replay now
Ultrasound: Clinical and administrative efficiency
- Hear from Roland Rott, President and CEO of Ultrasound, GE Healthcare, who shares his insights and perspectives on addressing ultrasound challenges, now and in the future. Ultrasound continues to lead innovation to solve for improving user and institutional efficiency as well as access to care.
- View now

Not all products or features are available in all geographies. Please check with your local GE Healthcare representative for availability in your country or region.

Koios DS is a trademark of Koios Medical.

BI-RADS is a registered trademark of the American College of Radiology.

REFERENCES

[1] Klibanov AL, Hossack JA. Ultrasound in Radiology: From Anatomic, Functional, Molecular Imaging to Drug Delivery and Image-Guided Therapy. Invest Radiol. 2015;50(9):657-670. doi:10.1097/RLI.0000000000000188

[2] https://www.fiercehealthcare.com/tech/investors-poured-4b-into-healthcare-ai-startups-2019

[3] https://hbr.org/2019/10/adopting-ai-in-health-care-will-be-slow-and-difficult

[4] https://www.sciencedirect.com/science/article/pii/S200103702030372X

[5] https://www.fiercebiotech.com/medtech/fda-clears-koios-medical-s-ultrasound-reading-ai-spots-breast-thyroid-cancer-two-seconds

[6] Lentz B, Fong T, Rhyne R, Risko N. A systematic review of the cost-effectiveness of ultrasound in emergency care settings. Ultrasound J. 2021;13(1):16. Published 2021 Mar 9. doi:10.1186/s13089-021-00216-8

[7] Lentz B, Fong T, Rhyne R, Risko N. A systematic review of the cost-effectiveness of ultrasound in emergency care settings. Ultrasound J. 2021;13(1):16. Published 2021 Mar 9. doi:10.1186/s13089-021-00216-8

Insights

Article

Improving clinical outcomes within ultrasound’s digital ecosystem

Specialties

Radiology

Topics

Artificial Intelligence (AI)

Care Delivery

Thought Leadership