PUBLISHER: Grand View Research | PRODUCT CODE: 1941489
PUBLISHER: Grand View Research | PRODUCT CODE: 1941489
Ultrasound AI Market Overview
The global ultrasound AI market size was estimated at USD 1.82 billion in 2025 and is projected to reach USD 13.56 billion by 2033, growing at a CAGR of 28.55% from 2026 to 2033. Rising demand for accurate and early disease detection, rapid growth in imaging volumes, and government initiatives and regulatory support are significant factors contributing to market growth.
In addition, growing emphasis on cost-efficiency in healthcare delivery and technological advancements in machine learning, NLP, and computer vision are some other factors fueling market growth further.
The increasing demand for precise early disease detection is driving market growth. The rising global prevalence of cancer, cardiovascular, and neurological disorders underscores the need for advanced imaging to facilitate timely interventions. Moreover, AI-powered analysis is essential for identifying subtle anomalies that are likely to be overlooked during manual review. For instance, according to a meta-analysis of 18 studies published in the AJMC in December 2025, found ultrasound-based AI models diagnose ovarian cancer more accurately than sonographers, with an internal validation sensitivity/specificity of 0.95 and AUC 0.98 of 95% and external validation AUC 0.91 compared to sonographers exhibiting a sensitivity of 0.83 (95% CI, 0.62-0.94), a specificity of 0.84.
In addition, automated measurements and risk assessments enhance diagnostic reliability by reducing inconsistencies among radiologists and supporting personalized therapies. Recent developments, such as Exo's April 2024 FDA-cleared AI tools for heart failure and lung evaluation on the Exo Iris portable ultrasound, illustrate technological progress. The prioritization of value-based care further accelerates the integration of AI ultrasound solutions across healthcare systems.
Moreover, technological advancements in machine learning, natural language processing, and computer vision fuel the growth of AI in the ultrasound industry. Deep learning algorithms are improving in accuracy, enabling more reliable interpretation of complex imaging modalities. For instance, according to a study published in the Nature Scientific Reports Journal in January 2026, Researchers developed a deep learning model using squeeze-and-excitation capsule networks and convolutional bidirectional LSTM (CBLSTM) for diagnosing gallbladder diseases from ultrasound images. This approach analyzes sequences of frames to detect gallstones, cholecystitis, and other disorders with high accuracy, outperforming traditional manual methods.
Global Ultrasound AI Market Report Segmentation
This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2021 to 2033. For this study, Grand View Research has segmented the global ultrasound AI market report based on component, application/modality, end use, and region: