The common bottlenecks which might slow down your business growth
High volumes of unstructured data make the traditional medical imaging process tedious with an urgent need for automated AI medical imaging software solutions.
Healthcare medical imaging accessibility is at an all-time low with the number of appointment backlogs in the current system.
With the shift from fee-for-service to value-based payments, there is a severe drain on medical imaging revenues and reimbursement rates.
When a medical imager is merely involved in translating the relevant image, holistic medical information is amiss.
AI medical imaging technologies are gradually exploding and the market is rife with competition.
No longer is it possible to satisfy today’s digital and aware customers with basic services.
Operational excellence achieved through automation of data gathering and mining by leveraging AI in medical imaging technology.
A virtual assistant is empowered with massive amounts of data and images as a reference guide and through intelligent algorithms, AI for medical imaging offers immediate clinical decision support toward a diagnosis. This is executed through the meaningful interpretation of medical images and the culling of latent information to distinguish patterns. Our algorithms allow for the structuring and mining of data through Electronic Medical Record (EMR) technologies.
Transforming the age-old method where a medical imager observes and translates a scan with full and final dependency on his interpretation is a thing of the past. AI for medical imaging technologies offers machine learning capabilities in AI image analysis software, which allows training data to identify patterns and abnormalities that surpass the ability of human radiologists. Deep learning technologies are deployed to speed up this process and make it possible in real-time.
Medical image professionals now have a virtual assistant. By adopting AI in medical imaging, companies can incorporate image analysis solutions that offer advance deep learning computer vision software for automated analysis. The results are far superior to traditional methods and offer a significantly higher rate of accuracy and speed.
The predictive analysis technology can make great strides in increasing the overall efficiency of imaging equipment utilization. The likelihood of errors is mitigated and a misdiagnosis can be completely avoided. Embedding this solution for AI in medical imaging companies will execute identical processes as human involvement, but with higher efficiency.
Comprehensive research applications are deployed into our intelligent algorithms technology to recognize familiar and unfamiliar textures in medical images and offer meaningful suggestions toward its implications. These are more often than not missed out by radiologists and medical image examiners. Computer-aided diagnosis has never been easier through the merger of AI and medical imaging.
As opposed to the traditional method of physical analysis and labelling of medical images into relevant categories, our data augmentation techniques of AI and medical imaging restrict the software from adopting comfortable structures and categories and force the use of performance data that lies outside of the immediate box. This enhances the exposure of the software to offer results that are of higher probability and efficiency.
OSP has worked with Stephen to create a mobile health application offering 'Doctor on Demand'. This mhealth solution is based on the Uber model to enhance the availability of health access in the US.
AI in Medical Imaging can take up tasks like aortic valve analysis, pulmonary artery diameter, and carina angle measurement. Automating the detection of cardiovascular abnormalities can offer quicker decision-making and fewer diagnostic errors. It can be used as a smart initial screening tool for cardiomegaly.
Injuries like hip fractures in elderly patients get worsened due to reductions in mobility and associated hospitalizations. Using AI in medical imaging to identify hard-to-see bone fractures, dislocations, or any soft tissue injuries may help specialists to be more certain in their treatment choices.
AI in Medical Imaging Software Solutions has the potential to improve the speed and accuracy of neurological disease diagnoses has focused on identifying new biomarkers. The advanced solution can flag images that indicate suspect results and offer risk ratios of ALS or PLS in the images.
AI can help enhance accuracy and apply quantitative imaging features to more precisely categorize microcalcifications and possibly decrease the rate of unnecessary biopsies of cancer patients. Providing risk scores for the areas of concern could allow doctors and patients to make more informed clinical decisions about how to proceed with treatment.
We have infused automated, intelligent imaging analytics into our AI medical imaging solutions into a cloud-based platform to transform the traditional model of medical imaging. Through deep learning, this technology can examine medical images and offer diagnosis at a much speedier rate than human diagnosis. This is done through reconstruction of regular images into 3D imaging. Superior level of patient imaging, eradicates the possibility of misdiagnosis, significantly improving overall image quality, and thereby reducing non-profitable repeat scans.
At OSP Labs, our AI medical imaging solutions combine image analysis with big data analysis to create a whole slide image for microscopic analysis that is infused with superior level analogies of artificial intelligence. Radiologists can receive automated suggestions into the history of similar images and their consequent diagnosis, along with summative pointers of related cases. Patients who are at high risk for cancer can be identified and preventive measures can be adopted.
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 through AI medical imaging software solutions. 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.
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