Machine Learning in the Medical Industry

Background

With the advent of machine learning, realms affecting lifestyles to industries have undergone major changes. A technology where computers are taught to imitate human decision-making skills according to Dhunay (2019) has also made its way it the healthcare industry. Over the years, machine learning has taken over businesses and visibly resulted in higher revenue generations and profits. Charts and statistics show how significantly businesses incorporating machine learning have taken over their respective industries. (Murphy, 2019).

Among the various other applications of machine learning, one such is the healthcare industry. The medical industry lays a huge scope and opportunities for machine learning to fulfill. This industry is very vital for a country’s growth and prosperity and plays an important role in the economy and GDP. Machine learning is continuously being incorporated by companies in studying and analyzing genetic data and making insightful interpretations (Gabutt, 2015). Machine learning is used in the very first stage of treatment, disease diagnosis, where the symptoms are used to classify the disease. Various systems and tool are developed and are used by doctors to cross-check their prediction as well as by patients and companies manufacturing wearables. Following diagnosis comes the drug for its treatment. Drug discovery and development involves various stages and spans over 13-15 years until a drug is ready to be marketed. The various stages involve some degree of predictability which is efficiently performed in silico reducing cost and time compared to in vitro. Machine learning is used in various stages of drug discovery using the –omics data (e.g. Chemogenomics) in lead discovery, virtual screening, target fishing, chemogenomics and determining the safety measures. The huge repositories generated from the various hospitals and institutions are mined upon and analyzed using data mining and machine learning techniques to extract insightful information. There still lies huge scope and future opportunities for machine learning in this industry to provide better patient care at reduced costs.