Machine Learning is Transforming Industry and Healthcare

Written by Chloe Harwood

Machine learning is an exciting new field that is much more than just a buzzword. It’s an area of research and development in computer science that has already begun to transform the way people and software interact. The applications of ML technology promise to disrupt countless industries, including healthcare, infrastructure, and financial services. In this post, we will discuss three major examples of how ML can and will change the business and operations of major industrial stakeholders. These are only a few examples of ML and more are on the horizon.


Oversight of critical utility networks is a difficult and complex task. Whether it is resource distribution, balancing, or maintenance, there are many different ways utilities can fail without good monitoring. It is hard enough to track the many users and producers of resources and the quality of the transportation technology, but recent times have revealed the threat of hacks and cyber attacks aimed at utilities. That means there is demand for better software support for utility managers. Human operators have to learn the theory and idiosyncrasies of their system to understand when and how to spot problems. Software that uses ML can absorb this kind of institutional knowledge faster and more accurately than a human. It can learn how to balance loads and what presages a potential breakdown, the most efficient setups for various use conditions, and other key concepts. Software doesn’t sleep, need breaks, ask for time off, or draw overtime pay. It’s not infallible, and it probably needs some degree of human oversight for safety and security. But creating AI managers for utilities and powering them with ML could reduce costs and increase efficient distribution.


One of the most exciting possibilities in machine learning is creating software that is capable of diagnosing patients. For example, a program that uses ML to diagnose cancer would first be “trained” by scanning in as much case history of cancer as possible. The software would use every recorded piece of medical data to try to learn what signals are associated with the likelihood of developing cancer. Machine learning tools can absorb information and data much faster than humans, so such a program would be able to have access to more potential signals about cancer. Then this program would analyze its data to identify key signals and start using that information on current patients. It’s like having a doctor who has a perfect memory for every case of cancer every recorded. This would allow for earlier detection of cancer as well as fewer wasteful tests in cases where they are not necessary. It would also promote customized care for patients by letting the software compare current patients to historical ones who are similar in terms of demographics and background. This is an important step because currently medical studies tend to under-sample minority groups, which can cause diagnostic misfires when those results are applied to vulnerable minority populations. Machine learning will mean faster and more accurate diagnoses, which implies cheaper, better healthcare for everyone.

Fraud Prevention

Detecting fraud and identity theft might seem like a different task from diagnosing patients with medical conditions, but in application there are actually a lot of similarities. They both use predictive analytics to determine something unknown from known parameters. In the case of fraud, that’s tapping into financial data and using those predictive analytics to assess whether a new transaction is likely to be fraudulent or not. Banks use this kind of software to see if new purchases are unusual. For example, they might take place in a different city from where the account holder usually lives or be strange relative to their usual purchases, like a tank of gas for someone who usually never buys gas or any other car-related products. The software that assesses transactions for fraud has to be both fast and accurate. If it isn’t fast, it will slow down consumer transactions and the bank could lose business as customers get annoyed. If it isn’t accurate, it will flag ordinary transactions as fraudulent or, worse, declare a transaction ordinary when it was in fact fraudulent. That combination of constraints is perfect for ML, which can rapidly absorb data, devise the fastest and most accurate form of analysis, and produce consistent results. In addition, machine learning software can easily adapt to new trends and changes in consumer behavior. Using its access to the records of consumer purchases, a bank’s ML algorithm can stay current on what kinds of spending are normal and what kinds likely indicate fraud. For example, cyber-criminals who steal credit card numbers might use them to purchase bitcoins to launder the money. An ML system would be able to see that a customer who never purchased a crypto-currency before suddenly maxed out their card on bitcoin and would flag that transaction as potentially fraudulent.

It’s hard to predict how machine learning is going to evolve because companies and industries are only now starting to incorporate it or elements of it into their business models. However, these examples show that the introduction of ML to an industry can be transformational in terms of cost and capability. This is especially compelling in fields where humans have to make a lot of judgment calls based on past information, because ML algorithms can provide recommendations generated by enormous amounts of data. ML and other explainable AI concepts are here to stay, because they have the potential to significantly increase decision-maker’s ability to act effectively across many different work domains. Explainable AI represents a new kind of innovation. People are accustomed to better machines replacing manual labor, but ML can replace mental labor in fields like medicine, law, and management that were previously resistant to automation. Not only are ML tools becoming more powerful and sophisticated, but they are easier to access thanks to improved frontends and more widespread use. Use of their techniques will only continue to rise as more industries understand their value and discover more ML applications.

About the author

Chloe Harwood