Artificial Intelligence in MRI Market Size and Trends

The global artificial intelligence in MRI market is estimated to be valued at USD 5.80 Bn in 2024 and is expected to reach USD 8.76 Bn by 2031, exhibiting a compound annual growth rate (CAGR) of 6.1% from 2024 to 2031.

Artificial Intelligence in MRI Market Key Factors

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The artificial intelligence in MRI market is expected to witness a positive growth trend over the forecast period. Proactive government initiatives in favor of digital healthcare and growing adoption of AI-based solutions by healthcare providers worldwide are some key factors expected to drive the demand for AI-enabled MRI systems. In addition, the increasing popularity of precision medicine approach and the need for accurate diagnosis is further augmenting the incorporation of advanced AI capabilities in MRI imaging. The development of deep learning algorithms and availability of large imaging data are also supporting the market growth. However, lack of skilled workforce and concerns regarding data privacy and safety may slightly inhibit the market progression during the forecast years. Overall, the artificial intelligence in MRI market is poised for significant gains with growing reliance on advanced medical imaging solutions.

Technological advancements in MRI systems

Technological advancements in MRI systems are poised to revolutionize the global artificial intelligence in MRI market. MRIs have become more powerful and sophisticated over the years, producing high resolution scans and images that provide invaluable diagnostic information to radiologists and physicians. However, the complexity and volume of data generated by modern MRI machines has grown exponentially. Manually analyzing the extensive imaging studies has become very time consuming and laborious for medical professionals. This is where artificial intelligence is playing a transformative role. AI tools that utilize deep learning algorithms and neural networks are being developed to automatically read, interpret, and highlight areas of interest from MRI scans. This augments and enhances the diagnostic capabilities of radiologists. AI applications can detect subtle anomalies, quantify tissues and lesions, and compare a current scan to historical scans to identify any changes. They can perform these tasks much faster than humans by analyzing thousands of data points simultaneously. For example, an AI solution approved by the FDA in 2020 reduced MRI reading time from 15 minutes to just 45 seconds per scan. This represents a significant increase in productivity and efficiency for healthcare providers. As the data volumes and cognitive loads associated with advanced MRI continue growing exponentially, AI is expected to play an even greater role by assisting with primary diagnosis, monitoring disease progression, treatment responses and facilitating personalized care pathways. AI also helps increase the availability of specialized expertise to underserved regions by enabling general radiologists to supplement their skills. The global artificial intelligence in MRI market is estimated to register a CAGR of over 30% until 2025, according to projections by the World Health Organization. This rapid growth reflects increasing investments from MRI manufacturers and diagnostic centers looking to optimize workflows and diagnostic accuracy through cutting-edge AI applications.

Growing use of AI-based diagnostic solutions

The use of artificial intelligence (AI) based diagnostic solutions is increasing rapidly in the global MRI market. AI is helping radiologists and healthcare providers extract more value from MRI scans by enhancing image quality, automating routine diagnosis, and enabling real-time clinical decision making. This is improving diagnostic accuracy and reducing reading time significantly. For example, algorithms developed by startups like Anthropic are automating tasks like lesion detection from brain MRIs for conditions like cancer, multiple sclerosis, etc. This allows radiologists to confirm detection in few minutes compared to manually examining hundreds of images. Some solutions are also automating MRI reporting by analyzing patterns in scans and generating preliminary radiology reports with important clinical findings highlighted. Such AI applications are reducing workload on overburdened radiologists and improving their productivity multi-fold. In the coming years, the adoption of advanced AI technologies like deep learning is projected to further accelerate. Models will become more sophisticated by training on growing volumes of MRI data from various healthcare providers as well as patients. This will enhance capabilities of AI solutions from the automated detection of new types of abnormalities to generating detailed radiology reports on par with human experts. It will also enable real-time augmented intelligence where AI will be used during scans to provide live assistance to radiologists.