ARTIFICIAL INTELLIGENCE IN ULTRASOUND IMAGING MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024-2031)

Artificial Intelligence In Ultrasound Imaging Market Size and Trends

The Global Artificial Intelligence in Ultrasound Imaging Market is estimated to be valued at USD 972.1 Mn in 2024 and is expected to reach USD 1,879.2 Mn by 2031, exhibiting a compound annual growth rate (CAGR) of 9.9% from 2024 to 2031.

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Factors such as increasing applications of AI in medical imaging and diagnosis, rise in awareness regarding benefits of early disease diagnosis, and growing funding & investments for developing AI-driven ultrasound solutions are expected to boost the demand for artificial intelligence in ultrasound imaging market. The market is also witnessing technological advancements with integration of deep learning and machine learning algorithms in ultrasound imaging systems and devices to offer enhanced accuracy and effectiveness. Adoption of AI-based ultrasound systems enables automation in routine tasks, which otherwise require trained technicians or sonographers to perform, allowing healthcare providers to focus more on critical tasks. This improves workflow efficiency while reducing healthcare costs. Rising focus on developing affordable point-of-care and portable ultrasound devices integrated with AI is also expected to support the market growth during the forecast period.

High adoption of artificial intelligence technologies

The increasing adoption of artificial intelligence technologies across various healthcare applications has been a major factor fueling the growth of the artificial intelligence in ultrasound imaging market. Ultrasound imaging has widely adopted AI capabilities to improve diagnosis and provide more accurate results. Various AI algorithms are being developed that can learn from vast amounts of ultrasound imaging data and help radiologists and sonographers in diagnosis. For example, many solutions now use deep learning to help detect abnormalities or perform automatic analysis of ultrasound scans. This reduces workload and can help catch subtle anomalies that may be missed by humans. As the capabilities of AI enhance, its integration in ultrasound is playing a critical role in improving patient care. Going forward, the trend of incorporating AI into clinical decision support systems will continue to rise. Many big technology companies and startups are actively working on developing advanced AI tools targeted at improving ultrasound imaging workflows. It is expected that within the next few years, most new ultrasound machines will come equipped with some form of embedded AI. This will allow automated analytics at the point-of-care without needing separate solutions. As per the World Health Organization, noncommunicable diseases account for over 70% of all deaths globally. Since ultrasound is a critical tool for screening and diagnosis of many such conditions, AI can help address this growing healthcare problem by enhancing ultrasound's abilities.

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Growth in applications of ultrasound imaging

The applications of ultrasound imaging are growing exponentially which is directly fueling the growth of artificial intelligence in ultrasound imaging market on a global scale. Ultrasound imaging has transformed from being used only for obstetric procedures to now having applications in varied medical disciplines including cardiology, radiology, urology and many others. This increasing scope of use across specialties has translated into higher demand for ultrasound devices and procedures. AI is helping improve and expand the capabilities of ultrasound devices to deliver more accurate diagnoses. Technologies like machine learning and deep learning are enabling ultrasound machines to produce high quality images, perform advanced quantitative analysis and provide diagnostic assistance to radiologists. For example, AI algorithms can detect abnormalities in ultrasound scans of hearts, livers, ovaries, etc. and highlight areas which require the radiologists' special attention. This is increasing the efficiency and throughput of ultrasonography. Several ultrasound device manufacturers have also integrated AI capabilities like automated image optimization and anomaly detection into their new models.