Artificial intelligence in healthcare has unimaginable potential. Within the next two years, with the help of Big Data, it will revolutionize every area of our life, including medicine. It will completely redesign healthcare, and for the better. Let's take a look at the promising solutions it offers.
There are many thought leaders who believe we are experiencing the Fourth Industrial Revolution. A "revolution" characterized by technologies that are merging the physical, digital and biological world. It is like a tsunami wave that affects all disciplines, economies and industries and even the biological boundaries of the human being. I am sure that healthcare will be the main industrial area of this revolution and the main catalysts for change will be artificial intelligence and Big Data.
And when I say Big Data, I mean very Big. As digital capacity evolves, more and more data is produced and stored online. The amount of digital data available is growing at an astonishing rate, doubling every two years. In 2013, it comprised 4,4 zettabytes: by 2020, the digital universe will reach 44 zettabytes or 44 trillion gigabytes. The world of Big Data is so vast that we will need artificial intelligence (AI) to be able to keep track of it.
Artificial intelligence ... real
We have not yet reached the state of "real" artificial intelligence, but AI is ready to sneak into our lives without big announcements or fanfare. It's already in our cars, Google searches, Amazon suggestions, and many other devices. Crab Apple, Cortana from Microsoft, Google OK Google and services Echo Amazon's take advantage of natural language processing to do useful things. Guy? Search for a restaurant, get directions, or memorize a meeting eavesdrop on our conversations (in this case it is useful to them).
But there is already more.
A 19-year-old British programmer launched a bot last September, DoNotPay, which is successfully helping people appeal their fines. He is an “AI attorney” who can decide what to do with the parking ticket received based on a few questions. As of June, the bot successfully appealed for 160.000 of the 250.000 parking tickets in both London and New York, with a success rate of 64%.
Imagine this efficiency in healthcare!
Combined with Big Data, AI in healthcare and medicine could better organize patient journeys or treatment plans, and provide doctors with all the information they need to make a good decision.
I'm not talking about a distant future.
For sure sophisticated learning and artificial intelligence algorithms will find a place in healthcare in the next few years: I don't know if it's two years or ten, but it's coming.
Andy Schuetz, senior scientist at Sutter Health.
There are already several great examples of AI in healthcare that show potential implications and possible future uses that spark optimism. Of course, it will only be a real revolution if these technologies are available to everyone, and not just the richest or the most experienced. Anyway, let's take a look at the future of artificial intelligence in healthcare.
Deepmind Healt, medical records at supersonic speed
The most obvious application of artificial intelligence in healthcare is data management. Collecting them, storing them, tracking them: it is the first step to revolutionize existing health systems. Recently, the AI search branch of search giant Google launched its project Google Deepmind Health, which is used to extract medical record data and deliver better and faster healthcare services. The project is in its infancy, but promises havoc.
Design of treatment plans
IBM Watson launched its own special program for oncologists which is capable of providing physicians with extremely refined treatment options. Watson for Oncology has an advanced ability to analyze the meaning and context of structured and unstructured data in clinical notes and reports that can be critical in selecting a treatment path. By combining attributes from the patient record with clinical experience, external research and data, the program identifies potential treatment plans for a patient.
Assistance in repetitive tasks
Also IBM launched another algorithm called Medical Sieve. It is an ambitious long-term exploratory project to build the next generation of 'cognitive assistants'. A range of AI with analytical and reasoning skills and a wide range of clinical knowledge. Medical Sieve is qualified to assist in clinical decision making in radiology and cardiology. The health assistant is able to analyze Big Data such as radiological images to identify and detect problems more quickly and reliably. Radiologists in the future may only have to examine the more complicated cases where human supervision is useful.
Big data and deep learning diagnostics
The medical start-up Enlitic It also aims to combine deep learning with vast Big Data archives to improve diagnostics and patient lives. Until recently, computer diagnostic programs were written using predefined sets of hypotheses about the specific characteristics of the disease. A specialized program had to be designed for each part of the body and only a limited set of diseases could be identified. Programs often oversimplify reality, resulting in poor diagnostic performance. The advent of Big Data will allow for enormous precision. Deep learning can manage a broad spectrum of diseases throughout the body and all imaging modalities (X-rays, CT scans, etc.) ”.
Hybrid consultation modes (live and online)
You have a headache, you feel lightheaded and you are certain that you have a fever. You should hear from a doctor. Call, talk to a secretary, ask for an appointment in two days. This is what won't happen with new medical care apps. Also in Italy they are being born, but I am talking about a reality already established as Babel. The English app offers online medical advice and subscription healthcare. Since this year, it offers medical advice with AI based on the patient's personal medical history.
Users report their disease symptoms to the app, which compares them to a Big Data disease database using speech recognition. After considering the patient's history and circumstances, Babylon offers an appropriate course of action. The app also reminds patients to take their medications and follows them along to find out how they feel. With such solutions, the efficiency of diagnosing patients can increase, and the waiting time in doctors' offices can be reduced.
Virtual nurses
We welcome the world's first virtual nurse. Molly was developed by the medical start-up Sensely. She has a smiling, lovable face coupled with a pleasant voice, and her unique focus is on helping people monitor their condition and treatment. The interface uses machine learning to support patients with chronic conditions between doctor visits. Provides proven, personalized follow-up monitoring and assistance, with a strong focus on chronic diseases.
Medication monitoring and treatment protocols: AiCure
There is also a specific solution to monitor whether patients are really taking their medications. The app AiCure, supported by National Institutes of Health English, uses a webcam and smartphone AI to independently confirm that patients are adhering to their prescriptions. This is very useful for people with serious medical conditions, for those who tend to go against doctor's advice, and for clinical trial participants.
AI, Big Data and Precision medicine
AI and Big Data will also have a huge impact on genetics and genomics. Deep Genomics aims to identify Big Data patterns of genetic information and medical records, looking for mutations and links to disease. A new generation of computational technologies is emerging that can tell doctors what will happen inside a cell when DNA is altered by genetic variation, both natural and therapeutic. What will we call them?
Craig Venter, meanwhile (one of the fathers of the Human Genome Project), works on algorithms that could design a patient's physical characteristics based on their DNA. With his latest feat, Human longevity, offers its (mostly wealthy) patients full genome sequencing coupled with a full body scan and very detailed medical check-up. The whole process makes it possible to detect cancer or vascular diseases in their early stage.
Big Data for the Creating drugs
The development of pharmaceuticals through clinical trials sometimes takes more than a decade and costs billions of euros. Speeding up this process and making it more affordable would have a huge effect on healthcare. Atomwise it uses supercomputers that pull therapies out of a Big Data database of molecular structures. Last year Atomwise launched a virtual search for existing and safe medicines that could be redesigned to treat Ebola. This analysis, which would normally have taken months or years, was completed in less than a day. Being able to fight deadly viruses months or years faster can be a big hit against the next pandemic. Or this one, why not.
Another great example of using Big Data for patient management is Berg Health, a biopharmaceutical company that extracts data to find out why some people survive disease, and then improve current treatments or create new therapies. They combine AI with patient biological Big Data to map the differences between healthy and disease-friendly environments and help in the discovery and development of pharmaceuticals, diagnostics and healthcare applications.
Analysis of a health system:
97% of healthcare bills in the Netherlands are digital and contain treatment, doctor and hospital data. These invoices could be easily recovered. A local company, Zorgprisma Publiek analyze invoices and use IBM Watson in the cloud to extract data. They can tell if a doctor, clinic, or hospital repeatedly makes mistakes in treating a certain type of condition in order to help them improve and avoid unnecessary hospitalizations of patients.
Things to do for AI and Big Data to really help us: MANY
First of all we need to break down prejudices and fears about AI, and help the population understand how we can limit the risks that the use of AI entails. The greatest fear of all is that artificial intelligence gets out of hand, controlling (or fighting) its "creators". Stephen Hawking put artificial intelligence on the list of dangers to the survival of the human race.
I do not think the situation is so bleak, but I agree with those who stress the need to prepare adequately for the use of AI in healthcare. We need a few things to avoid problems:
- Creation of applicable and mandatory ethical standards for the entire healthcare sector.
- Gradual development of AI to allow time to prevent any disadvantages.
- Medical professionals: training on the functioning of AI in medical contexts.
- Patients: habit of AI and less and less distrust.
- For those who develop AI solutions: more communication to the general public on the potential benefits and risks.
- For health authorities: measure the success and effectiveness of the system.
If we are successful, Big Data and AI will bring us enormous medical and therapeutic breakthroughs not from time to time, but on a daily basis.
Bianca Stan - Graduated in Law, writer with several books published in Romania and journalist for the group "Anticipatia" (Bucharest). It focuses on the impact of exponential technologies, military robotics and their intersection with global trends, urbanization and long-term geopolitics. He lives in Naples.
