You may have heard that nowadays a new tech called Machine Learning (ML) is extensively used in the healthcare industry. But do you know exactly what it is?
For some, the answer is very awkward. They may say, “Like a new technology, or a smart way of processing the data.” And you are very close.
To know more, take your time and go through this article. We will go a little deeper and explain the ins and outs of Machine Learning in the healthcare sector. You will enter a world of smart health records, drug discovery, clinical trials, and research – a new world of smart technology where Machine Learning is at the core of the tech revolution.
This article may interest those who want to enter the business of the healthcare industry applying Machine Learning or for anyone else who is curious enough to learn about something that is reshaping the healthcare sector.
The best way to learn about the application of Machine Learning in the healthcare industry is to look at examples. Below are a few examples of Machine learning in neuroscience.
A group of scientists, headed by Danielle Bassett, an American physicist, and systems neuroscientist, is using Machine Learning to come up with new treatments for psychiatric illnesses. [See “Harnessing Networks and Machine Learning in Neuropsychiatric Care.”] The scientists aggregate large datasets and synthesize the result of different imaging studies. As a result, they make diagnoses and predict treatment responses for different illnesses.
In another study, Penn Medicine researchers identify the size and shape of brain networks for children. The study helps to understand psychiatric disorders. It enrolled 700 children, adolescents, and young adults and applied Machine Learning techniques to analyze massive data. The study shows that functional neuroanatomy can be different and may be refined as children grow.
To put it simply, Machine Learning works with data to provide algorithms with self-learning neural networks. These algorithms are applied to analyze external data and apply that to a patient's case. For example, they may take data from X-rays, CT scans, various tests, and screenings.
At MD Anderson Center, the first medical Machine Learning algorithm is developed to predict acute toxicities among patients that have head and neck cancer and are undergoing radiation therapy. The accuracy of such results has been shown to be equivalent to that of an experienced radiologist.
If Machine Learning uses data to create algorithms, a question arises, ''Where does the data come from?'' Sometimes researchers and hospitals have access to high-quality data. However, this is not always so.
Let's take the example of the hospital mortality rate. As Mark Sendak, MD, population health and data science lead at the Duke Institute for Health Innovation says, the data is not always available. During their research, they found that they don't actually have complete death data, especially for patients who died out of hospitals.
So, the first challenge is to have accurate data. Once the data is there, it undergoes automatic manipulation by software with the use of natural language processing. In other words, natural language, like speech and text, undergoes computer processing to come up with meaningful patterns.
There are plenty of ways the healthcare sector can benefit from Machine Learning technology. Here are some of them.
Machine learning makes it possible to diagnose diseases that would otherwise be hard to diagnose. The IBM Watson for Genomics is a primary example. The research can help in making fast diagnoses including anything from cancers that are hard to detect at the initial stage to other genetic diseases.
The covid-19 pandemic is a primary example of the deficiencies of the current drug manufacturing system. Often drugs need to be made available at a faster speed than it is possible nowadays. The pandemic opened the eyes of researchers and practitioners to the current deficiencies of medical science. Since Machine Learning allows finding patterns that would otherwise be unnoticed for the human brain, new possibilities come up with the development and manufacturing of new medicines. The data-driven decision-making of ML can speed up the drug development process as well as reduce failure rates in drug discovery and development.
Today, healthcare is becoming smart, like smart cities, smart appliances, and many other smart devices. Nowadays there are many Machine Learning techniques that are applied in healthcare services, like patient monitoring, early diagnosis of disease, and so on. Healthcare is becoming extensively informatized and personalized. This change is reflected in medical model changes like preventive healthcare. We witness a shift from general management to personalized management, and a tendency to focus on preventive healthcare rather than disease treatment.
Other benefits of Machine Learning technology in healthcare include:
- Smart health records
- Clinical trials and research
- Smart data collection
- Prevention of epidemics
- Personalized medicine.
It is not a secret that Machine Learning reduces the cost for hospitals. For example, hospitals can reduce readmission rates. Doctors can take data of a similar problem with other patients and react accordingly. A physician can apply the appropriate treatment before the patient gets sick.
Here are some other ways hospitals can benefit from ML.
- Increasing efficiency of health records
- Predicting drug effects and potential problems based on previous data
- Managing work time at hospitals.
If this is so, it's not surprising that a lot of hospitals want to adopt machine learning apps. How much does it cost to implement ML in healthcare?
Since ML goes hand in hand with Artificial Intelligence, this data can help you understand how much it costs to implement ML in healthcare.
AI Type Cost
Custom AI solution $6000 to $300,000 / solution
Third-party AI software $0 to $40,000 / year
No wonder we see a proliferation of AI and ML apps in the healthcare sector nowadays. If you have such an idea, see what an Addevice, an Armenian software development company can offer. We can come up with a preliminary estimate and help you create anything out of scratch. Your role is to come up with the idea. We will do the rest.
Gone are those days when doctors had to consult piles of paper in order to treat or diagnose a patient. Digitalization has been taking place since the early 20th century. The beginning of the digital age is estimated to be 2002 when humankind started storing more information in digital rather than in analog format.
However, digitalization is only part of the story. There should be a single database that can be analyzed to come up with patterns and faster treatment and diagnosis. And this is where Machine Learning comes.
We are definitely moving in the direction of Machine Learning applications. However, the path is not easy. Much of the data today is encrypted and restrictions are posed on access to protect patient privacy. A lot of procedures are legally regulated. Machine Learning healthcare app development requirements are another field of study for those who want to develop ML apps.
Whether or not we can create a better healthcare system or not is a matter of the future. One thing is clear that there should be a compromise between the privacy of data and healthcare efficiency. Whether or not we want to move to disease prevention instead of disease treatment is challenging.
The healthcare system is rigid. Most of the decision-makers prefer simpler systems in which they can have more control. The ''pen and paper'' mentality is still to be overcome for AI and ML to bring real results.
Despite all limitations, artificial intelligence and machine learning are undoubtedly the future. The challenge is to ensure that these models do not endanger the human job market. It is necessary to find some kind of balance between the ''thinking machines'' and humans. Irrespective of what old school guys think, the transformation of the currently defective healthcare system is imperative. There is a need to revolutionize the whole system that is far from being perfect.