Global computer vision in healthcare market was estimated to be US$ 473.42 million in 2020 and is anticipated to reach US$ 16573.33 million by 2030, growing at an estimated CAGR of 48.5% from 2021 to 2030. Increasing number of imaging procedures for identifying chronic diseases is expected to aid the overall market growth. The market participants are still concentrating on developing new AI products.
The progress in computer vision evolution has enabled extensive use of medical imaging data for more accurate diagnosis, treatment, and disease prediction and prevention. By using computer vision technologies, healthcare professionals (hospitals and clinics amongst others) can acquire enhanced medical information that is not only interpreted to establish a diagnosis and prescribe medication but can also be used for disease prediction and analysis report generation. Healthcare specialists can leverage the power of computer vision to automate the medical report generation. Feeding and analyzing data from X-rays, ultrasound, CT scans, and MRI to computer vision algorithms, clinicians will be able to gain in-depth insight into an individual's physical condition, predict when a disease will develop, and when appropriate treatment will be needed. It has been duly reported that image information accounts for over 90 percent of all healthcare input data. It opens up a multitude of opportunities to train computer vision algorithms to improve both patient care and the efficiency of the healthcare industry as a whole. Automation of processes relying on image recognition can increase the quality of care while reducing the need for human input to complete tasks. The capital-intensive demand is more compared to the labor-intensive growth and this overall reduces human errors improving the quality of care for the patients in general.
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The healthcare providers comprising of the hospitals, clinics and ambulatory research centers amongst others captured over 40% of the overall market in 2020 and is expected to hold the highest share over the forecast period. The Orlando Health Winnie Palmer Hospital for Women & Babies executed AI technology that used pictures taken with an iPad device and analyzes images of surgical sponges and suction canisters. As the hospital delivered 14,000 babies per annum, doctors used to often overestimate how much blood women lose during delivery. With the help of computer vision they can understand the amount more accurately, allowing them to treat the women appropriately. However, the largest challenge is implementation especially for C-section surgeries as this engages some complications or bottlenecks; otherwise, the computer vision technology works extremely well for the hospital applications.
The computer vision may assist not only in the coronavirus (COVID-19) detection but also with diagnosing the COVID-19-related pneumonia. The usage of digital x-ray imagery with the computer-aided diagnosis can be used for the detection of COVID-19 features as it is used for different diseases such as cancer, tumor detection and cardiovascular diseases. However, the visual detection of COVID-19 features is challenging at first and requires pre-processing the visuals to enhance the poor contrast of soft tissues in x-ray imagery. Multiple deep learning computer vision models exist for x-ray based COVID-19 diagnosis. The most popular one for detecting COVID-19 cases with chest radiography images is named COVID-Net and was developed by Darwin AI, Canada. It is trained with a dataset referred to as COVIDx and is based on a total of 16’756 chest x-ray images across 13,645 patient cases. The COVID-Net computer vision model achieved almost 92% accuracy for COVID-19 diagnosis. The Computer tomography is considered one of the most efficient methods for COVID-19 detection.
Computer Vision in Healthcare Market:
- By Application
- Tumor Detection
- Medical Imaging
- Cancer Detection
- Medical Training and Clinical Trials
- Combating Covid-19
- Health Monitoring
- Machine-assisted Diagnosis
- Timely Detection of Disease
- Remote Patient Monitoring and Lean Management in Healthcare
- Others
- By End User
- Healthcare providers
- Diagnostic centers
- Academic research institutes
- Private and public research centers
- Others
- By Component
- Hardware
- Processors
- CPUs
- GPUs
- FPGAs
- Al accelerator products
- Others
- Memory devices
- Networks
- Software
- Cloud-Based
- On-Premise
- Services
- By Geography
- North America
- US
- Canada
- Mexico
- Rest of North America
- Europe
- France
- The UK
- Spain
- Germany
- Italy
- Nordic Countries
- Denmark
- Finland
- Iceland
- Sweden
- Norway
- Benelux Union
- Belgium
- The Netherlands
- Luxembourg
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- New Zealand
- Australia
- South Korea
- Southeast Asia
- Indonesia
- Thailand
- Malaysia
- Singapore
- Rest of Southeast Asia
- Rest of Asia Pacific
- Middle East and Africa
- Saudi Arabia
- UAE
- Egypt
- Kuwait
- South Africa
- Rest of Middle East & Africa
- Latin America
- Brazil
- Argentina
- Rest of Latin America
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