How Artificial Intelligence Enhances Ultrasound in Clinical Practice

Artificial intelligence is becoming part of routine ultrasound imaging because it can help address one of the modality’s biggest challenges: variability between users. By supporting repetitive and technically demanding steps such as image optimization, Doppler setup and measurement placement, AI can help make the LOGIQ™ ultrasound workflows more efficient and consistent.

Why is abdominal ultrasound so operator dependent?

Ultrasound is dynamic and interactive. During one exam, the user scans multiple organs and adapts settings for each one. Typical actions include:

• Adjusting gain, depth, and focus
• Switching between imaging modes
• Optimizing Doppler settings
• Placing measurement markers

These steps are repeated across structures such as the liver, kidney, aorta, and bile duct. Each organ requires different settings. For example, Doppler parameters used for the aorta will not work for slower flow in the kidney. This is why experience plays such a large role. As Anita Martin, Global AI Product Manager at GE HealthCare, explains: "There are many challenges today related to efficiency and varying skill levels. We want to use the power of AI to help address these challenges¹."

How does AI recognize organs during scanning?

AI systems are trained on large sets of ultrasound images. During scanning, the LOGIQ system:

• Analyzes the image in real time
• Identifies patterns such as shape and texture
• Matches them to known anatomy
• Suggests what structure is being viewed

In practice, this allows the LOGIQ system to understand what the user is scanning and adapt the workflow accordingly.

Why is Doppler optimization challenging?

Color Doppler requires balancing several settings at the same time. Key parameters include:

• Velocity scale, which controls sensitivity to flow
• Frame rate, which affects motion smoothness
• Image detail, which affects resolution
• Noise control, which affects clarity

Improving one setting can worsen another. For example, increasing sensitivity may introduce noise. In addition, each organ requires different Doppler settings.

• Large vessels like the aorta need higher velocity scales
• Smaller or slower flow, such as in the kidney, needs lower scales

Manual adjustment takes time and varies between users.

How does AI improve Doppler imaging?

AI simplifies Doppler optimization by linking it to anatomy. Once the system recognizes the organ, it can:

• Select appropriate Doppler settings
• Adjust the region of interest
• Apply optimized parameters automatically

In a workflow evaluation cited in the accompanying white paper on the LOGIQ Portal, AI-supported abdominal ultrasound tools showed measurable reductions in setup time and system interactions.:

• Up to 46 percent reduction in time spent on color flow setup
• Around two-thirds fewer system interactions
• Consistent optimization and reliable placement of the region of interest

As Anita Martin notes: "We use our applications to target key areas such as efficiency and image quality, so we can support users in their daily scanning activities¹."

For clinicians, this means faster setup and more consistent Doppler imaging across different users and exams.

How does AI reduce workflow complexity?

A standard ultrasound exam involves many repeated steps. These include switching modes, adjusting settings, and preparing the image for measurement.

AI reduces these steps by automating parts of the workflow:

• Automatic preset selection
• Automatic positioning of the region of interest
• Real-time parameter adjustment

In practice:

• Measurements can be performed up to 65 percent faster
• The number of system interactions can be reduced by up to 80 percent

Dr Chris Harvey, Consultant Radiologist, highlights the impact: "Overall, 89 percent of AI-generated measurements were accepted, which is really very good¹." This shift reduces repetitive actions and allows clinicians to focus more on interpretation.

How does AI assist with measurements?

Measurements are a key part of  ultrasound. They can also be a source of variability. LOGIQ AI supports this by:

• Detecting the target structure
• Outlining it automatically
• Placing measurement markers

This approach is used for structures such as:

• Kidney size
• Abdominal aorta diameter
• Common bile duct diameter

Instead of starting from scratch, the clinician reviews the suggested measurement and adjusts it if needed. Dr Chris Harvey adds: "This is going to save us a significant amount of time, reduce keystrokes, improve workflow efficiency, and hopefully reduce repetitive strain injuries¹."

Can clinicians still adjust the results?

Yes. Clinical control remains essential. AI provides a starting point, but the user can:

• Move measurement markers
• Adjust boundaries
• Confirm or reject the result

Different modes of assistance can also be used in LOGIQ ultrasound, from fully automatic to suggestion-based. This ensures that AI supports the workflow without limiting clinical decision-making. As Professor Adrian Lim notes from clinical use: "Once you’ve used it, you want it on all the time¹."

Where is AI most useful in abdominal imaging?

AI brings the most value in tasks that are repetitive or technically demanding. Common use cases include:

• Measuring the abdominal aorta
• Assessing kidney size
• Evaluating the bile duct
• Performing Doppler studies

In these situations, consistent settings and reproducible measurements are critical. AI helps reduce variation and improve reliability across exams. To summarize, AI is changing abdominal ultrasound by simplifying complex tasks. It improves:

• Workflow efficiency
• Measurement consistency
• Image standardization

Most importantly, it reduces the need for repeated manual adjustments. This allows clinicians to focus more on image interpretation and patient care, rather than system operation. But it is not only about saving time. Repeated manual adjustments and long scanning sessions can also contribute to physical and cognitive fatigue. By reducing system interactions and automating repetitive steps, AI may help support a more efficient and less demanding scanning experience.

To continue exploring the topic, visit the AI section of the LOGIQ portal, where you can access the workshop recording and see these capabilities in action. You can also review the accompanying white paper for a deeper look at the technology, its clinical applications, and the evidence supporting its use. Together, these resources provide valuable insights into how AI is helping clinicians improve efficiency, consistency, and diagnostic confidence across a wide range of ultrasound examinations.

_{GE HealthCare Whitepaper: AI in Action: Efficiency Without Compromise (JB35051XX)}

¹ Martin, A., Harvey, C., & Lim, A. Artificial Intelligence in Ultrasound (Workshop recording, Version 2, JB37280XX). GE HealthCare, May 20, 2026.