In the healthcare sector, data scientists are in much demand. Data is the key to innovation and it opens up new opportunities for improved and more informed healthcare. It is the data scientist’s job to find ways to collect and process high volumes of healthcare data to further interpret and use it for medical discoveries.

The time is ripe to intern as a data scientist and gain experience in various sectors. For now, let us look at the use of data science to bring advancement in the healthcare sector.

Medical Image Analysis

Data science plays a major role in its application in medical imaging. Procedures such as detecting tumours become easier with it. The imaging techniques include X-ray, magnetic resonance imaging (MRI), computed tomography and mammography. Many new tools are being developed to provide more accurate ways of extracting data from the images with efficient quality. With the help of data science, image interpretation can be made seamless.

Artificial Intelligence (AI) improves the quality of image evaluation in radiology. All the time, doctors use their experience and knowledge to draw the right conclusions from images. With numerous images that have been processed and marked by a healthcare professional, a data scientist can train the neural network to recognize deviations in new images. The neural network model trained on a massive image collection from a database can then analyze a picture and conclude if there is a disease.

Genetics and Genomics

The advancement in genetics and genomics create many ways for personalized treatment. Data scientists try to understand the impact of DNA on our health and their goal is to find individual biological connections between genetics, diseases and drug response.

Data science techniques allow integration of various data with genomic data in disease research. This provides a greater understanding of genetic issues in reaction to particular drugs or diseases. When we would be able to acquire reliable personal genome data, we will achieve a deeper understanding of the human DNA.

Drug Discovery

Machine learning presents an excellent opportunity for biochemical and pharmaceutical industries in the field of drug discovery. The drug discovery process is generally very complicated and involves many disciplines. A single formula has to go through multiple steps before it is approved. On an average, it takes 12 years to make a submission. The formula is often rejected despite all the time, effort and money invested.

Data scientists can shorten the process, adding a perspective to each step from initial screening of drug compounds to the prediction of the success rate based on biological factors. Algorithms can forecast how the compound will act in the body using advanced mathematical modelling and simulations instead of experiments conducted in the laboratory. The aim is to create computer model simulations as a biologically relevant network simplifying the prediction of future outcomes with high accuracy.

Virtual Assistance for Patients

Due to the Covid-19 outbreak, a consolidated effort is being made to limit patients’ visits to the hospital and shift everything to virtual platforms. Effective solutions are being provided with the help of mobile applications which are bringing the doctor to the patient.

The mobile apps are AI-powered and can provide basic healthcare support usually as chatbots. The patient describes his/her symptoms or asks questions and in turn receives key information about his/her medical condition derived from a wide network that links symptoms to causes. Apps can also remind the patient to take medicines on time and if necessary, assign an appointment with the doctor.

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