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

Data privacy and security concerns are significantly restraining the growth of global artificial intelligence in ultrasound imaging market. With AI and deep learning being applied to ultrasound imaging, there are serious risks of patient data and images being leaked, stolen or misused. Ultrasound generates live video feed of internal organs and high-resolution images. If such sensitive data falls in the wrong hands, it can be misused for myriad unlawful purposes. This leaves patients and healthcare providers hesitant to adopt AI-powered ultrasound tools.

Lack of stringent data privacy and security regulations in many countries further exacerbates these risks. Patients are justifiably worried about how their personal health details like ultrasonography reports and scans containing anatomical details could be stored, shared and processed by third parties without proper consent or oversight. Even in developed nations, there are ongoing debates around establishing clear rules regarding ownership and management of patient data generated through AI applications. The uncertainty and fear of data breaches discourage both patients and hospitals from embracing new AI-based ultrasound technologies.

Market Opportunities: Application in new disease diagnosis

Artificial intelligence has opened up new avenues for disease diagnosis using ultrasound imaging. With the help of deep learning algorithms, ultrasound images can be analyzed to detect diseases more accurately. This represents a great opportunity for the global AI in ultrasound imaging market. AI has the potential to help radiologists and sonographers make faster and more reliable diagnoses, especially for conditions where ultrasound image interpretation may otherwise be challenging or ambiguous. Deep learning models can be trained on huge volumes of ultrasound images to recognize subtle signs that the human eye may miss. This can improve diagnosis of diseases like pneumonia, cancer, and cardiac abnormalities. For example, a 2021 study published in Nature presented an AI system that achieved an area under the receiver operating characteristic curve of 0.99 for detecting pneumonia from ultrasound videos, outperforming expert clinicians. The use of AI is also beneficial in emerging markets and rural areas that lack sufficient number of trained radiologists. Automated diagnosis using portable ultrasound machines integrated with AI could help bring quality healthcare to such underserved regions. As per WHO, accurate radiological diagnosis is not accessible to over half of the world's population. AI-powered ultrasound could help address this gap.