What is machine learning? According to Arthur Samuel, who popularized the term, ML-based software enables computers to learn from data improving themselves without being explicitly programmed. Machine learning (ML) is a category of algorithms that allows software applications to receive input data and statisticaly analyse it to update outputs each time new data becomes available. ML is a subfield of artificial intelligence (AI). Any technology user today has benefitted from machine learning.

With the ever-increasing volume, variety, and speed of data available, the scientific disciplines have provided us with advanced mathematical tools, processes, and algorithms to use this data in meaningful ways. Data science (DS), machine learning (ML), and artificial intelligence (AI) are three such disciplines. The question often arises: what is the difference here: data science vs machine learning vs AI? Can they be compared at all? Let’s figure it out.

The latest developments in medical care mean a lot of pros to everybody, beginning with reduced unnecessary disability and increased life spans to better health equity and life quality. Besides all those benefits, the changing healthcare landscape means a heavy burden, especially for the finances — the USA is planning to spend no less than $6 trillion, or about $17K per person, on healthcare in 2027.

How does ML respond to the real needs of healthcare organizations? According to research made by Syft in 2018, hospitals spend over $25 billion more than necessary in their supply chains despite having the ability to save an average of 17.7% in their total supply expenses. Artificial intelligence (AI) and machine learning (ML) can decrease costs spent on such stuff.​

VITech is pleased to reveal the results of the State of data science and ML in healthcare survey that we conducted on LinkedIn in 2019. The survey sought to look into the scope and patterns of data science and machine learning adoption in the healthcare industry. Over 50 qualified respondents represented a variety of company sizes, from startups to corporations with more than 10,000 employees, in the pharma, care sector, biotechnology, and medical device development. Among them: are C-level execs, directors, and VPs (50% of the pool), as well as ML engineers, data scientists, and software developers. The surveyed have a firm grasp of the industry’s challenges and objectives, enabling us to analyze and assess the trends and the actual state of tech in healthcare.

Healthcare companies — providers or payers — have historically relied on computers for administrative tasks. However, new use cases have emerged as technology matured and the industry digitized. Today, hardly any clinic operates without a fleet of computers to store and manage patient/facility data, monitor patients and equipment, perform operations, and research.

Can technology help us in the fight against the virus? There are already a lot of solutions developed to protect people from getting infected. For example, the “Stop Corona” app from the Austrian Red Cross society. It keeps an anonymous contact diary that logs personal encounters using a “digital handshake.” If somebody has symptoms of COVID-19, all those who “have digitally shaken hands” with that person will be automatically informed about one of their anonymous contacts showing signs of an infection. All these people are asked to isolate themselves to reduce the infection’s risk. This way, fewer people will be exposed to potential diseases. Additionally, this system supports and relieves doctors who would otherwise have to go through this process manually.

COVID-19 has been the hot topic for us over the last months, but now it looks like we are returning to something like a new reality. The whole world is searching for ways to prepare for this. And, of course, employers need help to enable their employees to a safe work environment as possible.

Artificial intelligence and ML-based solutions are changing medical care. Healthcare AI solutions are a reality in many fields and specialties of medical help. Machine learning (ML), deep learning (DL), natural language processing (NLP), and AI identify healthcare needs and help work faster, and with more accuracy. They use data patterns to make informed medical or business decisions quickly.

As the world progressively shifts digital, Artificial Intelligence (AI) has significantly altered the way humans work for the better.