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According to IBM, medical images contain around 90% of the overall medical data. Doctors are utilizing medical imaging methods to envisage body parts. A few image processing algorithms take the input image to improve, section, and denoise images. Descriptive image recognition algorithms are later leveraged to excerpt the insights and understand the results to propose better treatment solutions.
The drug discovery procedure is highly complicated. It costs around $2.6 billion and around 12 years to take it from the lab to the market. But right now, the algorithms and models have drastically minimized the laboratory work involved in the drug discovery process. Data Science has helped researchers to examine the outcome of chemical combinations easily to extract vital insights like genetic mutations, kind of cell, and several other details. Several unsupervised ML algorithms aid to discover improved drugs for the people.
Managing a huge amount of data generated in the healthcare industry is really difficult. It is in hand-written registers and so Data Science can be of immense help here. It will convert all the paperwork into a digital form by using numerous Machine Learning algorithms. The ML algorithms will aid to extract key insights from the existing patient data and then evaluate it with the data that is already stockpiled in the database to find the best treatment for the patient.
Data Science in healthcare has allowed doctors to foresee the events that take place during the treatment process. So, many serious diseases can be treated if detected at the right time. Therefore, foreseeing the diseases and the risks in the treatment will help to figure out better prevention plans.
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