Artificial Intelligence Market In Medical Imaging And Diagnostics Report
Growing Demand of Artificial Intelligence in Medical Imaging and Radio-Diagnostic - Global and Regional Market Analysis See less
Growing Demand of Artificial Intelligence in Medical Imaging and Radio-Diagnostic - Global and Regional Market Analysis
One of the most potential area of health innovation is the application of artificial intelligence in medical imaging and medical diagnostics. The report from Stratview Research provides insights into the opportunity, awareness, demand and the trends in segments such as machine learning in healthcare, integration of AI into radiology and more. Magnetic resonance imaging (MRI) and computed tomography (CT) collectively account for 70-80% of the market, followed by Neuroradiology and others.
Artificial Intelligence applications vary from image acquisition, processing to aided reporting, follow-up plan, data storage, data mining, and others. The report provides a balance between AI threats and opportunities for radiologists in the modern medical world. The use of machine learning incorporates computational models and algorithms that imitate the architecture of the biological neural network in brain, i.e., artificial neural networks (ANNs). Performance wise Deep learning has higher performance rate compared to traditional machine learning.
The increasing amount of data to be processed can influence how radiologists interpret images i.e. from inference to detection and description. When too many images are analyzed by the radiologists in a day, the chances of error increase, at the same time a radiologist is reduced to be a mere “image analyst”. The clinical interpretation of the findings is left to other physicians or specialists. In other words, if radiologists do not have time for clinical judgement or scenarios such as Indian or East Europe or African market where there is scarcity of radiologists, the final interpretation of radiological examinations will be left to non-experts in medical imaging. Under these circumstances, AI is a great support to medical imaging, infact AI algorithms can look at medical images to identify patterns after being trained in large number of examinations and images. Physicians in remote areas can actually switch to Bayesian decision making process with the help of AI. This market is bound to grow tremendously as AI can extract images features either visible or invisible to human eye, thereby increasing the specificity and sensitivity of the imaging in radio-diagnostics.
AI has the potential to replace many of the routine detection, characterization, and quantification tasks currently performed by radiologist using cognitive ability as well as accomplish the integration of data mining of electronic medical records in the process (EHR/EMR is a bigger market in itself).
Automating the detection of abnormalities in commonly ordered imaging tests, such as chest X ray could lead to quicker decision-making and fewer diagnostic errors. For e.g., using AI to identify left atrial enlargement from chest X ray could rule out other cardiac or pulmonary complications and help providers conclude a targeted approach while treating the subject. AI tools could be used to automate other measurement tasks such as Aortic valve analysis, carina angle measurement, pulmonary artery diameter, etc.
AI has scope in Orthopedic segment and could also be used to identify hard to find fractures (the fracture type is often difficult to detect on standard images). AI can help the physician separate the images on the basis of hard to see fractures, dislocations, soft tissue injuries and other conditions when compared to internal bleeding or organ injury. For e.g., a patient presenting head and neck trauma could be assessed for odontoid fracture by using AI radiology tool.
AI has a big scope in diagnosing neurological conditions. Degenerative neurological diseases such as amyotrophic lateral sclerosis (ALS) can be devastating diagnosis for a patient. Currently there is no cure for ALS and there are many similar neurological conditions where accurate diagnosis takes time, leading to higher cost of healthcare. Using AI tool, ALS and primary lateral sclerosis (PLS) can be easily demarcated and this can help the physician decide the treatment path in much more effective way. Currently such tools are not available. The demand for AI in neurological segment is expected to grow between a CAGR of 21-28% from 2020-2025 worldwide. Markets such as USA, Japan, China and few Western European countries will be the early adopters of such technology.
Autonomous AI has tremendous potential to lower healthcare cost, improve the quality of care, and make healthcare more accessible where patients are, rather than where hospitals are. For e.g., there is a good diabetic clinic in New Mexico, close to the Mexican border which adopted specialist diagnostic technology (using AI) into its primary care set up, helping with diabetes eye exam and surprisingly the clinic is run by trained nurses and healthcare workers. The report from Stratview Research looks into such case studies in the market which can be replicated at a low cost elsewhere in the global market. While developed markets such as North America, Western Europe and Japan have the necessary budget and inclination towards adopting AI in their imaging and diagnostics, the third world countries such as Nigeria, Gabon, Ethiopia, Istonia, Lativia, Laos, Sudan and other developing nations need it before anyone else, to cut down the cost of healthcare and bring care closer to patients. Companies such as Zebra Medical Vision, IBM, Arterys, Gauss Surgical Inc., Zebra Medical Vision, Sigtuole, Freenome, MIT and many more companies are already expanding their technologies and making them affordable in various market.
This report helps service providers consider such technologies and adopt them in their set ups in 2019-2020.