The complicated healthcare data is compiled, analyzed and delivered to support doctors in their diagnosis with the help of Artificial Intelligence and Machine Learning. Our complex algorithms and software become an important tool for doctors in reducing waiting time for the patients. The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes.
AI in Oncology is coming up in a big way in detection of cancers. The concentration and differentiating the bad cells from good cells combined with imaging scans is really helping the doctors to predict the Cancers fast.
Detecting blocked arteries, perforated veins by looking at retinal scans or other disorders of the heart and some parts of the circulatory system, AI is helping Cardiologists discover and treat heart diseases, much faster.
Detecting blindness early, with Retinal scans for diabetic patients . The damage to the blood vessels of the light-sensitive tissue at the back of the eye can now be detected early and doctors can now advise the patients with the help of AI, accordingly for Diabetic Retinopathy.
The specialty that has gained the greatest attention is the field of Radiology. An ability to interpret imaging results may aid clinicians in detecting a minute change in an image that a clinician might accidentally miss. Implementing AI in imaging is completely changing the diagnosis by scanning.
AI is at the core of our technology stack. Data Research Scientists continuously work on their data through AI to improve their findings. We’re working on some of the world’s most complex and interesting research challenges, with the ultimate goal of solving intelligence.
As the database is continuously growing, we are continuously working on the performance of our algorithms with the help of various tools and libraries to give you faster results, always.
The data is processed in nodes so that no one feels any lag when Exa/Peta bytes of data is accessed over the wire. We are continuously improving our Data Processing speeds.
We feed data to our algorithms, and the machines continuously work tirelessly to improve the findings ratio to 92-97% accuracy. They are always improving the outcomes of the diagnosis, working in the background.
Hospitals no longer will rely on their data to get patients from test to treatment, but they would have access to data from other hospitals, shared exclusively on cloud. At the same time their own data remain in their possession as they remain the prime “data controller”. Diagnosis rate ratio will increase.
Patients would get early diagnosis and would save them a lot of unnecessary tests. The treatment can start early and hence save lives in extreme cases.
Doctors would have tonnes of data at their disposal, with the click of a button. Their diagnosis rate increases and they would be able to support their diagnosis based on millions of records.
