AI in Ultrasound Imaging Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2021-2031

The Global AI In Ultrasound Imaging Market is projected to expand from USD 1.34 Billion in 2025 to USD 1.97 Billion by 2031, registering a CAGR of 6.63%. This technology integrates machine learning algorithms into sonography equipment to automate image acquisition, enhance visual quality, and aid in diagnostic analysis. The primary catalyst for this growth is the necessity to optimize clinical workflows and mitigate the impact of a shortage of skilled medical professionals, which creates a strong need for technology that augments human capabilities. Data from the American Society of Radiologic Technologists indicates that the vacancy rate for sonography positions in the United States reached 12.4% in 2025, a workforce gap that fuels the demand for automated solutions capable of sustaining high patient throughput and diagnostic uniformity despite staffing limitations.

However, a major obstacle hindering widespread market adoption is the difficulty of incorporating these advanced algorithms into older hospital information systems. The absence of standardized interoperability protocols frequently results in technical hurdles that block seamless data transfer between AI software and established electronic health records. Consequently, these connectivity issues discourage healthcare institutions from expanding their use of these solutions, effectively slowing the scaling of AI implementations in medical facilities.

Market Drivers

The surge in Point-of-Care Ultrasound (POCUS) utilization acts as a major market accelerator, as artificial intelligence increasingly makes diagnostic imaging accessible to non-specialists. By embedding AI algorithms directly into handheld devices to assist with probe positioning and image analysis, the technology effectively reduces entry barriers for emergency physicians, nurses, and primary care practitioners. This trend is highlighted by vigorous regulatory progress involving portable solutions; for instance, Exo announced in September 2024 via their "Revolution in Real Time" release that they secured FDA clearance for four new AI indications in 2024 alone, reaching a total of nine clearances and demonstrating the rapid commercialization of AI-enabled POCUS instruments.

Concurrently, increased corporate investment is speeding up the incorporation of deep learning models into traditional ultrasound systems. Leading medical technology companies are strategically acquiring specialized AI software firms to immediately enhance their legacy platforms with automated recognition and measurement tools. A prominent example of this consolidation occurred in July 2024, when GE HealthCare announced its agreement to acquire Intelligent Ultrasound’s clinical AI business for roughly $51 million, underscoring the industry's shift toward AI-driven efficiency. This momentum is further evidenced by regulatory data reported by MedTech Dive in October 2024, noting that the FDA had authorized a cumulative total of 950 AI or machine learning-enabled medical devices by August 2024, indicating a favorable environment for sustained growth.

Market Challenges

The difficulty of embedding AI algorithms within legacy hospital information systems serves as a significant restraint on the Global AI In Ultrasound Imaging Market. Medical infrastructure relies heavily on established Electronic Health Records and Picture Archiving and Communication Systems, which often lack compatibility with contemporary AI applications. This interoperability gap compels sonographers and radiologists to navigate fragmented workflows, frequently necessitating manual data transfers or switching between different workstations to view AI-derived insights. Such operational inefficiencies undermine the productivity benefits promised by automation, thereby diminishing the perceived return on investment for healthcare facilities.

As a result, the technical inability to ensure seamless data exchange fosters hesitation regarding the adoption of these advanced tools. According to the Healthcare Information and Management Systems Society (HIMSS), 41% of healthcare organizations in 2024 identified the integration of new solutions into current workflows as a major impediment to enhancing interoperability. This statistic highlights the widespread nature of the connectivity gap; as long as these technical hurdles remain, healthcare providers will continue to be reluctant to scale AI implementations, effectively stalling broader market expansion.

Market Trends

The adoption of Edge Computing for Instant On-Device AI Analysis is developing into a pivotal trend, fueled by the necessity for immediate clinical decision support devoid of cloud-processing delays. Manufacturers are increasingly integrating high-performance computing directly into ultrasound units, allowing complex algorithms to operate in real-time during scans. This capability facilitates the automated selection of ideal images and instant anatomical quantification, greatly improving workflow efficiency. For example, Fierce Biotech reported in August 2025 that Philips launched its Transcend Plus suite, which incorporates 26 FDA-cleared AI applications to deliver real-time, automated anatomical measurements for conditions like heart failure and valve disease, thereby enhancing on-device diagnostic speed.

Simultaneously, the market is witnessing a shift toward the Expansion of Specialized AI Algorithms for OB/GYN and Cardiology, moving focus from general image improvement to the detection of specific, complex pathologies. Developers are advancing beyond standard biometry to build deep learning models that can identify subtle structural anomalies often overlooked in routine exams, helping generalist operators navigate the steep learning curve of detailed fetal and cardiac assessments. In January 2025, the Society for Maternal-Fetal Medicine highlighted research showing that specialized AI software boosted detection rates for potential major congenital heart defects to over 97%, significantly surpassing standard detection methods.

Key Players Profiled in the AI In Ultrasound Imaging Market

• NVIDIA Corporation
• SmartAlpha
• Intel Corporation
• EchoNous, Inc.
• General Vision Inc.
• Lunit Inc.
• Agfa-Gevaert Group
• Butterfly Network, Inc.
• Enlitic, Inc.
• Micron Technology, Inc.

Report Scope

In this report, the Global AI In Ultrasound Imaging Market has been segmented into the following categories:

AI In Ultrasound Imaging Market, by Solution:

• Software Tools
• Services
• Devices

AI In Ultrasound Imaging Market, by Application:

• Radiology
• Cardiovascular
• Gastroenterology
• Obstetrics & Gynecology

AI In Ultrasound Imaging Market, by Technology:

• Machine Learning
• Natural Language Processing
• Context- Aware Computing
• Computer Vision

AI In Ultrasound Imaging Market, by Ultrasound Technology:

• Diagnostic Imaging
• Therapeutic
• 2D or 3D/4D Ultrasound Imaging
• High Intensity Focused Ultrasound
• Extracorporeal Shockwave Lithotripsy
• Doppler Ultrasound

AI In Ultrasound Imaging Market, by End Use:

• Hospitals
• Clinics
• Research Labs & Diagnostic Centers
• Others

AI In Ultrasound Imaging Market, by Region:

• North America
• Europe
• Asia-Pacific
• South America
• Middle East & Africa

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global AI In Ultrasound Imaging Market.

Available Customization

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• Detailed analysis and profiling of additional market players (up to five).

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