The healthcare industry benefits greatly from ML algorithms. Read this article to get to know what ML-based solutions can do for doctors and their patients!

Thanks to machine learning algorithms, healthcare services have become safer, quicker, more personalized, and efficient. ML-based tools can either replace human professionals or help them with their daily work. From this article, you’ll understand how exactly healthcare facilities and other businesses of the industry benefit from machine learning. This list is far from exhaustive because every year, professionals find more and more ways of using the technology.

Clinical Decision Support Systems

ML-powered clinical decision support tools can carry out the following tasks:

  • Analyze huge amounts of data to identify a disease
  • Decide on the next treatment stage
  • Determine any potential problems
  • Enhance patient care efficiency

Thanks to such tools, physicians can work faster and make fewer mistakes when diagnosing diseases and prescribing treatments. Plus, ML algorithms can accurately predict mortality rates: in the case of COVID-19 mortality, ML-based tools delivered 92% accuracy.

Smart Recordkeeping

Updating patient records is a repetitive and tedious task. But it’s highly important for effective decision-making and better patient care. To facilitate data entry, ML-based solutions equipped with optical character recognition technology can “read” physicians’ handwriting and transform it into digital format. Other algorithms can analyze this information and come up with treatment suggestions.

Medical Imaging

Until now, medical imaging relied on analog methods (such as X-rays). Such solutions can help diagnose diseases and find treatment for them — but they are not too efficient for anomaly identification, case grouping, and overall disease research. ML-based tools help human professionals interpret the images to differentiate healthy cells from tumors and better detect health issues.

Personalized Medicine

The term “personalized” doesn’t always mean that each patient should receive the treatment that suits them. People often have multiple conditions that require simultaneous treatment. ML algorithms help doctors to construct effective treatment plans, accounting for drug interactions and minimizing potential side effects.

Behavior Adjustments

Behavior adjustment is an essential part of preventive medicine. It suggests that people should abandon their unhealthy habits, opt for a balanced diet, get enough sleep and physical exercise, and so on. Factors such as obesity may be easier to identify, but both patients and their doctors might be unaware of unhealthy habits that are not so evident: for instance, if a person needs to sleep a bit less or drink more water. ML algorithms help people to find out about trends they would fail to notice otherwise.

Predictive Approach to Treatment

The earlier the doctor detects the disease, the easier and cheaper it should be for the patient to address it. ML-powered solutions effectively reveal the first signs of diabetes, oncology, liver, and kidney diseases. Doctors can choose an optimal treatment for the patient before their condition aggravates.

Data Collection

When a patient’s history contains a lot of data, it’s hard to know what information is relevant to a current case. The doctor will also need to ask many questions and spend time browsing the patient’s history. ML algorithms can quickly analyze the patient’s records and help the medical professionals compile the list of the most urgent questions. Instead of spending time looking for the information, doctors can focus on taking care of the patients.

Elderly and Low-Mobility Groups Care

Here are a few examples of what ML-powered tools can do for low-mobility groups, including the elderly and people using wheelchairs:

  • Improve their day-to-day lives with smart reminders
  • Schedule help
  • Determine optimal paths
  • Predict and avoid potential injuries by identifying common obstacles
  • Acquire help as soon as needed

So far, ML algorithms of this type are not too wide, but the demand for them keeps increasing. The Japanese authorities, for instance, are planning to have 75% of elderly care performed by AI.

Robotic Surgery

Some people feel psychologically uncomfortable when they think of surgical procedures being conducted by robots without human supervision. Yet so far, robots can only either work side by side with human surgeons or help them to prepare for medical operations. ML-based tools come in handy for surgery modeling and planning, evaluating the surgeon’s skills, and simplifying surgical tasks like suturing.

Drug Discovery and Production

ML-powered tools can analyze the previously acquired data on active components in drugs and how they affect the organism. When they come across a similar disease, they can model an active component that can help to cure the patient. In the future, this method can employ nanotechnology for better drug delivery. Personalized drug discovery and production are vital for patients with a unique set of illnesses or certain special requirements.

Clinical Research

Clinical research and trials require a lot of time and funds. If a manufacturer tries to accelerate the process, they might need to compromise on safety. Yet sometimes, patients can’t wait long (as happened with the sudden outbreak of COVID-19). ML algorithms can facilitate clinical research thanks to their ability to determine the best sample for the trial, gather more data points, analyze the ongoing data from the trial participants, and reduce the data-based errors.

Infectious Disease Outbreak Prediction

When the COVID-19 pandemic broke out, the global society turned out to be unprepared for it. Experts have been warning governments about the possibility of a pandemic for years. Yet they lacked the tools that would have been able to detect the signs of a pandemic at an early stage. To assess how likely the disease is to go out of control, such solutions can analyze satellite data, news, social media reports, and video sources.

Final Thoughts

Hopefully, you found this article informative and now you better understand the importance of machine learning health care. ML-powered solutions are indispensable for clinical decision support, smart recordkeeping, medical imaging, and behavior adjustments. They come in handy for infectious disease outbreak predictions, clinical research, drug discovery, and production. Thanks to machine learning algorithms, medical professionals can better personalize healthcare services and practice a predictive approach to treatment to prevent health conditions. In the foreseeable future, robotic surgeons might begin to outperform their human colleagues.