The common bottlenecks which might slow down your business growth
Volumes of disorganized patient data makes the imaging process tedious, and data mining a daunting task.
An increasing backlog of appointments, specifically in computed tomography (CT) scans is creating an adverse effect with regard to healthcare accessibility.
The transition from fee-for-service to a value-based payment model is creating confusion among reimbursement protocols and reducing overall profit.
Radiologists are constantly seeking to move from mere imaging interpretation to being involved more in the patient’s health analysis.
With the burst of technology in medical imaging, aggressive marketing by competitors is being adopted.
Patient satisfaction levels are at an all-time high due to the increasing standards of patient engagement.
Sifting through the volumes of patient information to identify relevant patterns is an almost impossible task for radiologists to undertake without a virtual assistant. AI in radiology technology is capable of offering immediate clinical decision support toward diagnosis through the meaningful interpretation of medical images and the culling of latent information to distinguish patterns, which can considerably enhance the work of radiologists.
The traditional method relies on the radiologist to read a scan with little or no help, with a full and final dependency on the identification of the radiologist. With our AI radiology technologies, a chest Computed Tomography (CT) scan is reviewed and potential findings are immediately identified from the image. Additionally, by combing through the patient history, related to the particular anatomy scanned, identification is made simpler.
Clinicians are forced to depend on medical image analysis performed solely by radiologists. Through our image analysis solutions in AI for radiology, computer vision software is powered with deep learning algorithms to undertake automated analysis, with highly accurate results that are delivered much faster than physical processes.
Radiologists are prone to errors due to imaging and human constraints. A misdiagnosis is a natural outcome of overloaded radiologists. Our breakthrough technology in AI for radiology offers deep learning and replicates the functions of the human brain to recognize images with higher than human level accuracy.
Through the use of comprehensive research applications, our intelligent algorithms are embedded with the ability to identify and analyze textures in medical images that are susceptible to be missed by the human eye. Computer-aided diagnosis has never been easier through the merger of AI and radiology.
Categorizing medical images is currently limited to the physical labelling of medical data. Our data augmentation techniques within AI and radiology curtail the memorization of training data and force the use of performance data that lies outside the training set. Augmentation in medical imaging creates a variety of scenarios that enhances the quality of training.
The introduction of bidirectional patient portals for AI radiology companies, a technology where patients are permitted to submit their own data and images into their EMRs, has witnessed a considerable reduction of costs of in-person clinic encounters and follow-ups. The features can also include activity tracking, physical monitoring, etc. that was previously limited to clinic visits.
By infusing AI radiology solutions into the medical imaging equipment, our software embeds advanced diagnostic techniques into the scanner, which provide automated diagnosis. We further offer image reconstruction solutions that leverage AI to deliver high-quality images from images that are formerly taken with poor quality dimensions. We have developed a 3D camera, which helps with patient imaging, eradicating the requirement of a higher dose, significantly improving overall image quality, and thereby reducing non-profitable repeat scans.
In direct contrast to the current cardiac diagnostic measures that are costly and tedious, we have developed intelligent AI radiology solutions that will benefit the medical imager and patient alike. Through the many potentialities of artificial intelligence, we have developed pathways that can significantly improve the current cardiovascular care domain. Cardiac imaging can be improvised through our technology that is aimed at assisting radiologists to gauge the presence of CAD, by identifying intrinsic signals scanned from the body without radiation, contrast agents or cardiac stress.
Artificial Intelligence technology can be applied to create further efficacies in lung cancer detection through our advanced algorithms that are aimed at providing effective low-dose CT screening. This is accomplished through intelligent algorithms embedded with the capability to identify the lesions that are cancerous and considerably reduce false positive rates. Our risk assessment tools accurately assess the difference between malignant and benign nodules.
We follow every government's regulatory mandate and create solutions that adhere to strict protocols.
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