The COVID virus has put immense pressure on the medical system, and people are looking to get some relief. It’s been a tough year for the medical system. With COVID, people are thinking about how to unload a heavy burden off hospital staffs’ shoulders.
In order to reduce the interaction between patients and doctors, we need a technical solution that would speed up decision-making in medical emergencies. We also have to make sure that all hospital staff members are properly protected from harm during these types of situations so they can provide high-quality care without worrying about their own safety or well-being.
How AI can help with the global COVID virus?
Recently, it’s been shown that AI tools such as Deep Learning (DL) can offer automated image interpretation and integration with clinical data. DL models are trained using large amounts of patient-specific training sets which in turn leads to improved diagnosis for patients suffering from COVID-19. This kind of treatment has resulted in better outcomes assessment too!
However, large amounts of data and computing power are required to train and optimize a DL model and data availability is one of the key bottlenecks in developing systems. Coupled with this, the lack of standardization of datasets makes it challenging to reuse existing AI tools in a country that is different from the one that it has been trained for, as its predictions are inherently biased. The data used for training models also needs to be shared systematically across countries so that they can be generalized appropriately and achieve more accurate results overall.
Examples of AI solutions
Beneath are some creative and innovative ways that Artificial Intelligence could help fight this treacherous code.
- Identifying if doctors are wearing personal protection equipment
- Measuring distance between people in a hospital. If it is shortened then an alert is generated and prevents viruses from being transmitted. Responsible parties are informed in order to correct their behavior.
- Apart from external interactions, one more tool is checking what happens inside of a human being. Connected to CT it is capable of detecting pathological changes inside of a chest. The process of combining chest imaging data with laboratory and clinical predictions to determine an individual’s outcome is made much simpler. We do continue our work in this particular direction while the results are rather promising.
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