ML in the healthcare industry. A boon? – Learnxt

ML in the healthcare industry. A boon?

ML in the healthcare industry. A boon?

Machine learning, in a way, has resurrected the healthcare industry. Though some doctors believe that no amount of technology can replace the human brain, technology assists humans to facilitate discoveries.
Machine learning has made inroads in the healthcare sector with a promise to stay. Radiology and pathology like imagery data are being used by most ML technologists. With the help of AI built machines, a semi-skilled person or a bedside nurse can also diagnose a defective organ accurately. Machine learning will have a significant impact in the areas where it will be possible to reproduce or standardise the data. In the coming time, it will also affect the practice period of the doctors.

Machine learning is a practical tool that can be brought into play for the health care industry. If machine learning looks forward to deploying in the healthcare industry, it must collaborate with a partner who is an expert in recognising areas of focus, elucidate noise, and lay more emphasis on the high possible areas of concern.

Why Machine Learning is a boon for the Healthcare industry?

By 2030, McKinsey expects ML in the healthcare industry to deliver global economic activity of around $13 trillion. Gartner,the research firm, sees the global AI-based economic activity in the short term to increase to $3.9 Trillion by 2022 from about $1.2 trillion in 2018.
The increasing number of machine learning applications in healthcare shifts our thoughts towards the future of the healthcare industry, where data analysis and innovation will work together to help the patients. Let’s have a look at the top machine learning applications in the healthcare industry:

Identifying Diseases and Diagnosis:

The most important application of machine learning in healthcare is the identification and diagnosis of diseases. Such ailments may include cancers that are hard to diagnose at the initial stages, genetic ailments, or any ailments which are otherwise hard to diagnose. IBM Watson Genomics, Berg, and P1vital’s PReDicT (Predicting Response to Depression Treatment) are leading examples of fast diagnosing of diseases and computing required clinical treatments for the same.

Drug Discovery and Manufacturing:

Early-stage discovery of drugs is one of the primary machine learning applications clinically, which helps in finding alternative therapies for multistage diseases. This includes R&D technologies such as precision medicine and next-generation sequencing. Currently, the machine learning techniques are designed in such a way that without any supervised learning and without providing any predictions, it can identify data patterns. Microsoft developed Project Hanover, which is using ML and AI-based technologies for cancer treatment and personalising drug combination for Acute Myeloid Leukaemia (AML).

Personalised Medicine:

By pairing the health of individuals with predictive analytics, personalised treatments, and disease assessments can get more useful. Currently, based on the symptomatic history of the patients, the doctors estimate the risk. But with machine learning, the physicians can choose from the set of specific diagnoses and avail the information required. IBM Watson Oncology leverages the medical history of the patient and generates suitable treatment options. In the coming years, we will get to see more such devices that will process more data to be available for machine learning-based technologies for the healthcare industry.

Smart Health Records:

Maintain up to date data records of patients can be a tiring process. Though technology had already done its part for making the data entry process easy, even now a majority of data entry processes take time. Machine learning is primarily required in the healthcare industry to save time, money, and effort required to complete a task and ease the process. Google’s Cloud Vision API and MATLAB’s machine learning-based handwriting recognition technology are slowly building up the document classification methods using ML-based OCR techniques and vector machines.

Machine learning with SRM- A boost your career needs!

Machine learning is helping the healthcare industry, patients, and stakeholders globally via its latest technology and applications. If we look deeply into machine learning, we can easily find specific cases in which machine learning algorithms have proven to be pivotal in healthcare and further expedited developing step by step process to execute them within the process.
Considering that Machine Learning is gaining momentum and has become a popular stream in the current uncertain scenario, SRM Group has launched LEARNXT, a global digital learning brand to empower people to achieve their dreams through education.
LEARNXT in collaboration with SRM IST and SRM AP will be offering various machine learning and AI in-demand courses. These programmes will primarily focus on equipping the principle application and concepts of ML AI through experiential learning.
Students get access to the concepts that are in line with the present and future needs and their learning tenure will be an amalgam of theoretical and experiential learning, where the latter remains imperative. At LEARNXT you will learn to pay attention to who, how, and what of machine learning and artificial intelligence.
Stay tuned to LEARNXT to know more about how machine learning applications and artificial intelligence algorithms are a boon to the healthcare industry.

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