The global AI in diagnostics imaging is USD 0.92 Billion in 2022. The market is further expected to reach a value of USD 20.11 Billion by end of 2032. The growth rate of AI in the diagnostics market in the forecast duration is 36.1%.
Implementation of artificial intelligence in diagnostic imaging will improve the efficiency of radiologists, pathologists, and other image-based diagnosticians. AI in diagnostic imaging will transform the healthcare industry with increased diagnostic accuracy, enhanced productivity, and improved clinical outcomes. Artificial intelligence in diagnostic imaging is anticipated to gain popularity in rapid growth in the forecast period.
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AI is capable of supporting the operations of the pathologist as they are effective in precisely performing medical imaging. AI in diagnostic imaging is expected to grow at a double-digit growth rate over the forecast period. AI in diagnostic imaging is achieving strong growth as the capability to detect cancer at an early stage and track tumor development. AI in diagnostic imaging will create significant time-saving in analyzing radiology reports.
Market players:
• Canon Inc.,
• GE Healthcare,
• Siemens Healthineers,
• DeepMind Technologies,
• Subtle Medical Inc.,
• Samsung Healthcare,
• Butterfly Network Inc.,
• Koninklijke Philips NV
AI in Diagnostic Imaging Market: Segmentation
• By product type
• Ultrasound scanners
• CT scanners
• MRI systems
• Optical coherence tomography devices
• Others
• end user
• Hospital
• Ambulatory surgery center
• Diagnostic center
• By region:
• North America
• Latin America
• Western Europe
• Eastern Europe
• Asia Pacific
• Japan
• Middle East and Africa
What’s stopping the use of AI in diagnostic imaging?
The field of medical imaging is revolutionizing the use of artificial intelligence in healthcare. However, it lacks clinical input, data privacy and security. Healthcare facilities must continuously monitor performance tools to improve effective care delivery. AI solutions are often built without clinician involvement and can do more harm than good. Patient reluctance to use AI in healthcare is another issue, as patients are unfamiliar with online and machine-driven care.
Determining the right data set is a challenge for AI algorithms. There are concerns about the safety and privacy of patient information that may discourage patients from using technology to obtain treatment. The infrastructure needed to implement AI in radiology is costly, hindering the acceptance of AI in medical diagnostics.
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