Machine Learning is one of the most leading technology for the future of the Healthcare industry. In this article, you will get knowledge of some important implementations of the technology, as well as some forecasts.
Now, technology-enabled healthcare is a reality as smart medical tools become a popular thing. The healthcare industry greets innovation; that’s why the future of AI in healthcare is very good. Google has already started an algorithm that successfully recognizes cancer in mammograms, while scientists from Stanford University can recognize skin cancer thanks to Deep Learning. Artificial Intelligence is in charge of treating thousands of different data points, predicting risks and results with accuracy, as well as many other functions.
Diagnosis and disease identification.
It is good to start with this topic as ML is very good at diagnosis; truly, this is one of the most powerful areas. There are a lot of types of cancer and genetic illnesses that are difficult to detect; but, ML could handle many of them in the beginning stages. IBM Watson Genomics is an excellent example of that. This project is linking cognitive computing with genome-based tumor sequencing and gives help in getting a quick diagnosis. PReDicT (Predicting Response to Depression Treatment) from P1vital is working to build a practical way to bring AI to enhance diagnosis and treatment in regular hospitals.
Health records improvement:
Despite all these technological inventions, managing health records is still a trouble. Yes, it is much faster today, but it still needs a lot of time. Records could be classified by vector machines and ML-based OCR recognition methods. The best examples of that are Cloud Vision API from Google and ML handwriting recognition technology from MathWorks.
The prediction of diabetes:
Diabetes is of the most basic, and very serious, diseases. It not only harms a person’s health on its own, but it also makes many different serious illnesses. Diabetes often harms the kidneys, the heart, and nerves. Machine Learning could help to diagnose diabetes very fast, saving lives. Classification algorithms like KNN, Decision Tree, and Naive Bayes could be a support to create a system that predicts diabetes. Naive Bayes is the most effective among them in terms of performance and computation time.
Predicting liver disease.
The liver plays an important role in metabolism. It is vulnerable to illnesses like chronic hepatitis, liver cancer, and cirrhosis. It is a really difficult task to effectively predict liver disease managing huge amounts of medical data; however, there have already been a few important shifts in this area. Machine Learning algorithms like classification and clustering are earning the difference here. The Liver Disorders Dataset or the Indian Liver Patient Dataset (ILPD) could be practiced for this task.
Finding the best cure.
Another best application is using Machine Learning at the beginning levels of drug discovery for victims. Currently, Microsoft is utilizing AI-based technology in its Project Hanover, which tries to get personalized drug combinations for curing Acute Myeloid Leukemia.
Making diagnoses with the help of image analysis.
Microsoft is transforming healthcare data analysis with its InnerEye plan. This startup uses Computer Vision to prepare medical images to obtain a diagnosis. As technology grows, InnerEye is creating more streams in healthcare analytics software. Very soon Machine Learning will become more effective, and even large data points could be explained to make an automated diagnosis.
Machine Learning in Medicine is doing great improvement. IBM Watson Oncology is a unique leader in this field by giving various treatment programs that first examine a patient’s medical records. As advanced biosensors strike the mass market — providing more data for algorithms — everything will get even good when it comes to producing personalized treatment plans.
This is a very interesting area to observe. Giving tips on your daily activities to prevent cancer? That’s exactly what an application from Somatix, a B2B2C-based company, is doing. This application keeps track of the unconscious activities we do every day and alerts us to those that might be dangerous from the long-term perspective.
Medical research and clinical trial improvement.
It’s no mystery that clinical cases could take years to complete, with important investments required. ML can contribute predictive analytics to spot the most suitable contestants for clinical trials, based on circumstances like one’s history of doctor appointments or social media activity. The technology will also reduce the number of data-based failures and could recommend the best sample sizes to be tested.
Leveraging crowdsourced medical data.
Now, researchers have entrance to a huge amount of data made public by the victims themselves. This is the root of improvements in Machine Learning in Medicine in the future. Why is data analytics is very important in the healthcare industry? Well, a connection between Medtronic and IBM has already resulted in the capacity to decipher, accumulate, and make insulin data available in real-time. As the Internet of Things (IoT) grows, there will be even more chances. Also, public data will enhance the diagnosis process and the issuance of prescriptions for medication.
Talking of data analytics, in 2020 authorities have access to data from satellites, social media trends, news websites, and video streams. Neural networks could treat all of that and make judgments on epidemic outbreaks all over the world. Critical diseases could be snapped in the bud before they could truly cause huge damage. This is very important in Third World countries, as they want advanced medical systems. Probably the best case of this section will be ProMED-mail, an Internet-based reporting platform, which observes outbreak reports around the earth. Artificial Intelligence is also hugely implemented in Food Safety, supporting prevent epidemic diseases on farms.
Artificial Intelligence Surgery:
This is apparently the most impactful space for Machine Learning, and it will become much more common in the near future. You can divide robotic operation into the following categories:
- Automatic suturing.
- Surgical workflow modeling.
- Improvement of robotic surgical materials.
- Surgical skill evaluation.
Suturing primarily means sewing up an open wound. Performing this method automated performs the whole method shorter while taking away stress on the surgeon. Still, it is early to speak about operations that are individually performed by robots, they now can support and help a doctor managing surgical devices. In the upcoming 5 years, it is assumed to become a special industry with assets of about $39 billion dollars. When a medical process conducted, the robot will bring tools for the doctor with its robotic hands. This kind of work reduces surgical complications by 50% and reduces the time the victim stays in the operating room by nearly 20%. Machine Learning algorithms for healthcare data analytics also values and defines new possibilities for future operations, as it gather sales data on every Artificial Intelligence Surgery.