When we talk about human lives and health, any technologies that can give more efficient, helpful, and faster analysis to hand out a proper treatment plan in time are tremendously valuable. Artificial Intelligence and its subdivision Machine Learning is taking over the world right now. Every day more and more business use cases appear in tech news. Industries like finance and banking seem to be the best choices for the technology, but what about other industries? Is the healthcare industry different? Of course not. Speaking of AI in the medical field, we must realize the tremendous potential and the changes Machine Learning can bring to the healthcare industry. This Google Trends chart displays the rising popularity of AI in Healthcare:
In this article, you will learn how ML can transform both patient care and the administrative processes of the industry. We have already enough research to prove that Machine Learning often surpasses humans in the diagnosis of disease. Algorithms are already doing a better job spotting malignant tumors than actual radiologists. Machine Learning for healthcare technologies provides algorithms with self-learning neural networks that are able to increase the quality of treatment by analyzing external data on a patient’s condition, their X-rays, CT scans, various tests, and screenings. Also, worth mentioning, deep learning is now largely used for detecting cancer cells. The model is given tons of cancer cell pictures to “memorize” their look.
However, we are very far off from the total replacement of humans in medicine. This article is about the potential uses of AI in Healthcare and the challenges for wider adoption.
The potential of Healthcare with Machine Learning
Machine Learning is one of the most common subdivisions of Artificial Intelligence. It is aimed at “training” models with data. According to a survey by Deloitte of 1,100 US companies that were using Artificial Intelligence, 63% were focusing on Machine Learning. It is a broad technique that could have a practical use in many industries and use cases.
How is this related to healthcare? The use of ML could boost the organizational side of the industry. A regular nurse in the United States is spending 25% of her work time on regulatory and administrative activities. Technology could easily take over these routine tasks such as claims processing, revenue cycle management, and clinical documentation and records management.
In another survey by the Harvard Business Review, over 300 healthcare executives and clinical leaders claimed there is a problem with patient engagement. More than 70% of the respondents reported that less than half of their patients are highly engaged in the treatment process, while 42% of the respondents claimed that less than a quarter of their patients were highly engaged.
Greater patient involvement definitely brings better health outcomes for patients. Machine Learning can offer automated messaging alerts and relevant targeted content that provokes actions at important moments. Generally, there are a variety of ways that ML can personalize and improve the treatment process.
What Are the Precise Applications for Machine Learning and Artificial Intelligence in Healthcare and Medicine?
One of the best Machine Learning for Healthcare applications is a bot system that makes the treatment period much easier. A virtual nurse for patients acts as a voice-controlled healthcare assistant that provides information on many illnesses, health disorders, and medicines. An AI assistant is a very handy thing if the patient needs real-time advice and it might be difficult for him or her to get to the doctor. Data engineers are working on solutions for all medical activities that deal with overall health monitoring as well as helping cure or even prevent disease.
Except for the very popular usage of Chatbots, you should pay close attention to the implementation of Machine Learning in Healthcare algorithms in:
- Rare diseases
So, Machine Learning in Healthcare algorithms are mostly Artificial Neural Networks. For example, CNN (convolutional neural networks) perform image identification, detection, and recognition. These are a complicated system of artificial neuron layers linked to each other and pre-trained on a dataset of damaged cell pictures to “memorize” the appearances of harmful cells.
These medical specialties are a hard nut to crack for data scientists, as long as they demand the most complicated area of Machine Learning to include deep learning. It enables the creation of neural networks and the detection of cells damaging the organism (like cancer cells).
Machine Learning in Healthcare technologies in oncology search for the cells affected by cancer at an accuracy level comparable to that of an experienced physician. The work of a pathologist whenever they are examining organic fluids of patients (such as blood, urine, and also tissues) can be aided by Machine Learning’s capability to analyze better and faster. Human sight equipped with a microscope is not even half as fast in its analysis as compared to an automated model. In addition, we have to accept the fact that we still struggle with a lack of information on the origin of rare diseases and the link of these diseases to particular characteristics of people afflicted with an illness. Along those lines, some Machine Learning in Healthcare startups help the industry with new methods of analyzing patients’ photos and tracing features.
But of course, there are many more ways to use Machine Learning in Healthcare. Let’s look at them.
Top 12 Machine Learning Applications in Healthcare in 2020
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. Take a closer look at The Raven Surgical Robot in action:
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.
The biggest challenges for AI in Healthcare
|Data governance||Medical data is still personal and forbidden for access. However, according to a Wellcome Foundation survey in the UK, only 17% of public respondents are against sharing their medical information with third parties.|
|Transparent algorithms||The necessity for transparent algorithms is not only required to meet strict drug development regulations, but also in general, people need to understand how exactly algorithms generate conclusions.|
|Optimizing electronic records||There is still plenty of fragmented information between various databases that need more structuring. When this situation improves, it will lead to advances in personal treatment solutions.|
|Embracing the power of data silos||The healthcare industry should change its view on the value of data and the way it could bring value from the long-term perspective. Pharmaceutical companies, for example, are typically reticent to change their product strategies and research in the absence of immediate financial benefits.|
|Data science experts||Attracting more Machine Learning experts and data science specialists is super important for both the healthcare and pharmaceutical industries.|
The future of AI in Healthcare
There is an overall fear that Artificial Intelligence will eliminate jobs, but in reality, that couldn’t be further from the truth. It is estimated that in 2020 Artificial Intelligence will create more jobs than it will eliminate, as a result of automation. Furthermore, by 2025 there are forecasts that AI will create 2 million additional jobs. That’s because AI and ML only automate routine tasks, some far beyond human capabilities. Thus, this opens more possibilities for human experts to take over more sophisticated tasks.
According to Allied Market Research, the global AI healthcare market will reach $22.8 billion by 2023. We are talking about $150 billion annual savings for the healthcare industry, thanks to Artificial Intelligence and Machine Learning solutions.
|Application||Potential Annual Value by 2026||Key Drivers for Adoption|
|Robot-assisted surgery||$40B||Technological advances in robotic solutions for more types of surgery|
|Virtual nursing assistants||$20B||Increasing pressure caused by medical labor shortage|
|Administrative workflow||$18B||Easier integration with existing technology infrastructure|
|Fraud detection||$17B||Need to address increasingly complex service and payment fraud attempts|
|Dosage error reduction||$16B||Prevalence of medical errors which leads to tangible penalties|
|Connected machines||$14B||Proliferation of connected machines/devices|
|Clinical trial participation||$13B||Patent cliff; plethora of data; outcomes-driven approach|
|Preliminary diagnosis||$5B||Interoperability/data architecture to enhance accuracy|
|Automated image diagnosis||$3B||Storage capacity; greater trust in AI technology|
|Cybersecurity||$2B||Increase in breaches; pressure to protect health data|
Why is medical diagnostics often wrong?
Sure, doctors are not gods, nor magicians, and they can make mistakes; but usually, some ubiquitous factors contribute to the likelihood of a wrong decision. Among them are: inefficiently used data from health information technologies; communication issues between clinical workers, patients, and their loved ones; and, of course, the functioning of the healthcare system itself. The last one is definitely the hardest to change, but the solution for the first two reasons is already available.
Machine Learning in Healthcare Informatics
Machine Learning in Healthcare Informatics has potent analytical abilities. Thus, the electronic information provided to doctors is becoming much better. Doctors may easily access parameters such as the risk for stroke, kidney failure, and coronary artery disease. They get patients’ indicators on the basis of many blood pressure readings, gender, family history, race, and the latest clinical examination data. Afterward, valuable clinical insights are made to help doctors compose a treatment plan and provide the best care as a result. Possible outcomes help them estimate how much the procedure will cost — thus, making treatment more affordable.
Summarizing the importance of the advantages of Machine Learning in Healthcare, the highest score goes to its powerful abilities in sorting and classifying health data as well as speeding up doctors’ clinical decisions and any kinds of predictions that can save lives or make surgery less complicated (e.g., the prevention of hypoxemia during surgery). Isn’t that already a lot? Human life is without a shadow of a doubt the most valuable thing. At the current moment, ML in Healthcare provides technologies that directly contribute to the future of advanced medical diagnostics as well as the future of medicine. There are other solutions like AI in Nutrition, which we will talk about in future articles!
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