According to news, Machine Learning is one of the most prominent technology for the future of the Healthcare industry. But how real it is? Is there any significant value, or is it just optimistic forecasts? In this article, you will learn on some practical implementations of the technology, as well as some on-point predictions.
Today, technology-enabled healthcare is a reality as smart medical devices become a widespread thing. The healthcare industry welcomes the innovation; that’s why the future of AI in healthcare is very bright. Google has already launched an algorithm that successfully identifies cancer in mammograms, while scientists from Stanford University can identify skin cancer thanks to Deep Learning. Artificial Intelligence is in charge of processing thousands of different data points, predicting risks and outcomes with precision, as well as many other functions.
Diagnosis and disease identification.
It is fair to start with this point, because ML is very good at diagnosis; actually, this is one of the most effective areas. There are plenty of types of cancer and genetic diseases that are hard to detect; however, ML could handle many of them in the initial stages. IBM Watson Genomics is a great example of that. This project is combining cognitive computing with genome-based tumor sequencing and provides help in making a quick diagnosis. PReDicT (Predicting Response to Depression Treatment) from P1vital is trying to create a practical way to bring AI to improve diagnosis and treatment in regular hospitals.
Health records improvement.
Despite all these technological breakthroughs, keeping health records is still a hassle. Yes, it is much quicker today, but it still takes a lot of time. Records could be classified by vector machines and ML-based OCR recognition techniques. The leading 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 common, and very dangerous, diseases. It not only damages a person’s health on its own, but it also causes many other serious illnesses. Diabetes mostly damages the kidneys, the heart, and nerves. Machine Learning could help to diagnose diabetes very early, saving lives. Classification algorithms like KNN, Decision Tree, and Naive Bayes could be a basis to build a system that predicts diabetes. Naive Bayes is the most efficient among them in terms of performance and computation time.
Predicting liver disease.
The liver plays a leading function in metabolism. It is vulnerable to diseases like chronic hepatitis, liver cancer, and cirrhosis. It is a very hard task to effectively predict liver disease using enormous amounts of medical data; however, there have already been some significant shifts in this area. Machine Learning algorithms like classification and clustering are making the difference here. The Liver Disorders Dataset or the Indian Liver Patient Dataset (ILPD) could be used for this task.
Finding the best cure.
Another great application is using Machine Learning at the first levels of drug discovery for patients. Currently, Microsoft is using AI-based technology in its Project Hanover, which aims to find personalized drug combinations to cure Acute Myeloid Leukemia.
Making diagnoses via image analysis.
Microsoft is revolutionizing healthcare data analysis with its InnerEye project. This startup uses Computer Vision to process medical images to make a diagnosis. As technology evolves, InnerEye is making more waves in healthcare analytics software. Very soon Machine Learning will become more efficient, and even more data points could be analyzed to make an automated diagnosis.
Machine Learning in Medicine is making great progress. IBM Watson Oncology is a distinctive leader in this area by providing numerous treatment plans that first analyze a patient’s medical history. As advanced biosensors hit the mass market — providing more data for algorithms — things will get even better when it comes to creating 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 secret that clinical trials could take years to complete, with significant investments required. ML can offer predictive analytics to spot the best candidates for clinical trials, based on factors like one’s history of doctor visits or social media activity. The technology will also lower the number of data-based errors and could suggest the best sample sizes to be tested.
Leveraging crowdsourced medical data.
Today, researchers have access to an enormous amount of data made public by the patients themselves. This is the source of improvements of Machine Learning in Medicine in the future. Why is data analytics important in healthcare? Well, a partnership between Medtronic and IBM has already resulted in the ability to decipher, accumulate, and make insulin information available in real-time. As the Internet of Things (IoT) evolves, there will be even more possibilities. Also, public data will improve the diagnosis process and the issuance of prescriptions for medication.
Speaking of data analytics, in 2020 experts have access to information from satellites, social media trends, news websites, and video streams. Neural networks could process all of that and make conclusions on epidemic outbreaks all over the world. Dangerous diseases could be nipped in the bud before they could actually cause massive damage. This is super important in Third World countries, as they lack advanced medical systems. Probably the best example of this area will be ProMED-mail, an Internet-based reporting platform, which monitors outbreak reports around the globe. Artificial Intelligence is also greatly implemented in Food Safety, helping prevent epidemic disease on farms.
Artificial Intelligence Surgery.
This is probably the most impactful area for Machine Learning, and it will become much more popular in the near future. You can divide robotic surgery into the following categories:
- Automatic suturing.
- Surgical workflow modeling.
- Improvement of robotic surgical materials.
- Surgical skill evaluation.
Suturing basically means sewing up an open wound. Making this process automated makes the whole procedure shorter while taking away pressure on the surgeon. Even though it is early to talk about surgeries that are solely performed by robots, they now can assist and help a doctor manipulate surgical devices. In the next 5 years, it is expected to become a special industry with a capital of about $39 billion dollars. When a medical procedure is conducted, the robot will fetch instruments for the doctor with its robotic hands. This kind of practice lowers surgical complications by 50% and about decreases the time the patient stays in the operating room by about 20%. Machine Learning algorithms for healthcare data analytics also assesses and defines new opportunities for future surgeries, as it collects data on every Artificial Intelligence Surgery.
Originally posted here