Solving Healthcare Problems with Machine Learning


Machine learning and artificial intelligence have many applications, including many in the healthcare field. The foundation of machine learning is using large volumes of past data to predict future events better. In healthcare, this is a particularly important capability. Every day, doctors use various methods and tools to diagnosis their patient’s diseases. If machine learning can help improve and speed up the diagnosis process, this can result in reducing the time it takes for patients to start getting treatment. There are several ways for machine learning to be used in the healthcare system. A few examples are discussed below.

Improved Diagnostics

As already touched on, doctors and other medical professionals rely on a range of tools to analyze patients’ conditions and come up with a diagnosis. These include equipment such as MRI and tests such as blood and urine analysis. During these scans and test, data is generated. The doctor then analyzes this data or diagram to determine the ailment.

Machine learning can be used to improve and speed up the identification of diseases. By feeding the computer algorithm with disease information, computer software programs can be used to help predict disease. Genetic diseases, in particular, can be detected with the assistance of artificial intelligence programs based on trends from past data. This can lead to a greater chance of early detection in diseases such as cancer, which is potentially life-saving.

For medical image diagnosis, advanced software programs can also speed up the analysis of scans. This can help professionals such as radiologists to process medical images much faster. There are various other ways for machine learning to be used to diagnose diseases faster.

Drug Production

Another vital area in the healthcare system is the pharmaceutical industry. Drug companies are responsible for discovering new medicines and then manufacturing them to supply the world’s pharmacies and medical facilities. The process of drug discovery to approval is a long journey of many years. Only once the drug has gone through a series of clinical trials, it can be approved by regulating bodies.

Machine learning can be used in the drug lifecycle in various ways. Data generated during the drug discovery process can be analyzed using advanced algorithms. This can help generate new formulas to try and speed up the testing process. Machine learning is particularly useful when patterns in data can be found.

Medical Record-keeping

Medical records are important in the healthcare system because they allow doctors to keep track of their patients and their ailments and treatments. This is a big field, requiring accurate and up-to-date records. Machine learning can be applied to this part of the system to save both money and time. Some of the ways for artificial intelligence and machine learning to be used in the record-keeping system is through software that can recognize handwriting, provide treatment suggestions based on records, and other smart capabilities.

Personalized Medicine

It’s no longer one size fits all in the medical industry. Treatments such as operations have always been tailored to the patient, but not so much in the drug part of healthcare. Drugs are usually made in bulk, and the same drug is given to patients with the same condition. The main difference is usually the strength of the medication given. In the trend towards personalized medicine, machine learning is going to be important to handle the huge amounts of data required. Drugs are going to need to be tweaked slightly to cater to specific patients.

Personalized medicine also involves looking at all the data about a person’s health, genetics, and physiology to come up with a treatment plan that is specific for that person. This requires a lot of expertise and computer processing power, but the result is a better-fitted treatment for them.

--- ADVERTISEMENT ---

reset password

Back to
log in