There are various thought leaders who believe that we are experiencing the Fourth Industrial Revolution, which is characterized by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human.
I am certain that healthcare will be the lead industrial area of such a revolution and one of the major catalysts for change is going to be artificial intelligence.
Big Data and Artificial Intelligence Will Revolutionize our Lives
With the evolution of digital capacity, more and more data is produced and stored in the digital space. The amount of available digital data is growing by a mind-blowing speed, doubling every two year. In 2013, it encompassed 4.4 zettabytes, however by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes (!).
Usually, we make sense of the world around us with the help of rules and processes which build up a system. The world of Big Data is so huge that we will need artificial intelligence (AI) to be able to keep track of it.
We have not yet reached the state of “real” AI, but it is ready to sneak into our lives without any great announcement or fanfares – narrow AI is already in our cars, in Google searches, Amazon suggestions and in many other devices. Apple’s Siri, Microsoft’s Cortana, Google’s OK Google, and Amazon’s Echo services are nifty in the way that they extract questions from speech using natural-language processing and then do a limited set of useful things, such as look for a restaurant, get driving directions, find an open slot for a meeting, or run a simple web search.
But there is already more to that. A 19-year-old British programmer launched a bot last September which is successfully helping people to appeal their parking ticket. It is an “AI lawyer” who can sort out what to do with the received parking ticket based on a few questions. Up until June, the bot has successfully appealed between 160,000 of 250,000 parking tickets in both London and New York, giving it a 64% success rate.
Imagine This Efficiency In Healthcare!
AI in healthcare and medicine could organize patient routes or treatment plans better, and also provide physicians with literally all the information they need to make a good decision.
And do not think it is the tale of the distant future. “I have no doubt that sophisticated learning and AI algorithms will find a place in healthcare over the coming years,” Andy Schuetz, a senior data scientist at Sutter Health said. “I don’t know if it’s two years or ten — but it’s coming.”
And as winter finally arrived in the sixth season of Game of Thrones, we should be certain that we will gradually get there. Only by looking at how many companies are interested in AI in healthcare gives the impression that it is an area with a promising future. Although IBM’s Watson is the big dog in cognitive computing for healthcare, the race is on and the track is growing increasingly crowded. Dell, Hewlett-Packard, Apple, Hitachi Data Systems, Luminoso, Alchemy API, Digital Reasoning, Highspot, Lumiata, Sentient Technologies, Enterra, IPSoft and Next IT – Just to mention a few names.
There are already several great examples of AI in healthcare showing potential implications and possible future uses that could make us quite optimistic.
However, these solutions will only revolutionize medicine and healthcare if they are available to the average, mainstream users – and not only to the richest medical institutions (because they are too expensive) or to a handful of experts (because they are too difficult to use).
Let’s Peak Into The Future
Artificial intelligence already found several areas in healthcare to revolutionize starting from the design of treatment plans through the assistance in repetitive jobs to medication management or drug creation. And it is only the beginning.
Mining medical records:
The most obvious application of artificial intelligence in healthcare is data management. Collecting it, storing it, normalizing it, tracing its lineage – it is the first step in revolutionizing the existing healthcare systems. Recently, the AI research branch of the search giant, Google, launched its Google Deepmind Health project, which is used to mine the data of medical records in order to provide better and faster health services. The project is in its initial phase, and at present they are cooperating with the Moorfields Eye Hospital NHS Foundation Trust to improve eye treatment.
Designing treatment plans:
IBM Watson launched its special program for oncologists – and I interviewed one of the professors working with it – which is able to provide clinicians evidence-based 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 may be critical to selecting a treatment pathway. Then by combining attributes from the patient’s file with clinical expertise, external research and data, the program identifies potential treatment plans for a patient.
Assisting repetitive jobs:
IBM launched another algorithm called Medical Sieve. It is an ambitious long-term exploratory project to build a next generation “cognitive assistant” with analytical, reasoning capabilities and a wide range of clinical knowledge. Medical Sieve is qualified to assist in clinical decision making in radiology and cardiology. The “cognitive health assistant” is able to analyze radiology images to spot and detect problems faster and more reliably. Radiologists in the future should only look at the most complicated cases where human supervision is useful.
The medical start-up, Enlitic, which also aims to couple deep learning with vast stores of medical data to advance diagnostics and improve patient outcomes, formulated the perks of deep learning the following way: “until recently, diagnostic computer programs were written using a series of predefined assumptions about disease-specific features. A specialized program had to be designed for each part of the body and only a limited set of diseases could be identified, preventing their flexibility and scalability. The programs often oversimplified reality, resulting in poor diagnostic performance, and thus never reached widespread clinical adoption. In contrast, deep learning can readily handle a broad spectrum of diseases in the entire body, and all imaging modalities (X-rays, CT scans, etc.)
Getting the most out of in-person and online consultations:
You have a headache, you feel dizzy and you are sure that you have a fever. Your partner tells you that you do not look great, you should go to the doctor. So, you call the assistant of your GP and ask for an appointment. It turns out you have to wait two more days to get the chance for a visit. Now, this is what’s not going to happen with Babylon and its new app. The British subscription, online medical consultation and health service, Babylon launched an application this year which offers medical AI consultation based on personal medical history and common medical knowledge. Users report the symptoms of their illness to the app, which checks them against a database of diseases using speech recognition. After taking into account the patient’s history and circumstances, Babylon offers an appropriate course of action. The app will also remind patients to take their medication, and follow up to find out how they’re feeling. Through such solutions the efficiency of diagnosing patients can increase by multiple times, while the waiting time in front of doctor’s examining rooms could drop significantly.
Health assistance and medication management:
Everybody, please welcome the world’s first virtual nurse, Molly developed by the medical start-up Sense.ly. It has a smiling, amicable face coupled with a pleasant voice and its exclusive goal is to help people with monitoring their condition and treatment. The interface uses machine learning to support patients with chronic conditions in-between doctor’s visits. It provides proven, customized monitoring and follow-up care, with a strong focus on chronic diseases.
Also, there is already a solution for monitoring whether patients are taking their medications for real. The AiCure app supported by The National Institutes of Health uses a smartphone’s webcam and AI to autonomously confirm that patients are adhering to their prescriptions, or with better terms, supporting them to make sure they know how to manage their condition. This is very useful for people with serious medical conditions, for patients who tend to go against the doctor’s advice and participants in clinical trials.
Artificial intelligence will have a huge impact on genetics and genomics as well. Deep Genomics aims at identifying patterns in huge data sets of genetic information and medical records, looking for mutations and linkages to disease. They are inventing a new generation of computational technologies that can tell doctors what will happen within a cell when DNA is altered by genetic variation, whether natural or therapeutic.
At the same time, Craig Venter, one of the fathers of the Human genome Project is working on an algorithm that could design a patient’s physical characteristics based on their DNA. With his latest enterprise, Human Longevity, he offers his (mostly affluent) patients complete genome sequencing coupled with full body scan and very detailed medical check-up. The whole process enables to spot cancer or vascular diseases in their very early stage.
Developing pharmaceuticals through clinical trials take sometimes more than a decade and costs billions of dollars. Speeding this up and making more cost-effective would have an enormous effect on today’s healthcare and how innovations reach everyday medicine. Atomwise uses supercomputers that root out therapies from a database of molecular structures. Last year, Atomwise launched a virtual search for safe, existing medicines that could be redesigned to treat the Ebola virus. They found two drugs predicted by the company’s AI technology which may significantly reduce Ebola infectivity. This analysis, which typically would have taken months or years, was completed in less than one day. “If we can fight back deadly viruses months or years faster that represents tens of thousands of lives,” said Alexander Levy, COO of Atomwise. “Imagine how many people might survive the next pandemic because a technology like Atomwise exists,” he added.
Another great example for using big data for patient management is Berg Health, a Boston-based biopharma company, which mines data to find out why some people survive diseases and thus improve current treatment or create new therapies. They combine AI with the patients’ own biological data to map out the differences between healthy and disease-friendly environments and help in the discovery and development of drugs, diagnostics and healthcare applications.
Open AI helping people make healthier choices and decisions:
Did you ever hear the expression, open AI ecosystem? No? Don’t worry, it is rather new and a very fancy expression for connected AI infrastructures. However, the World Economic Forum named it as one of the top 10 emerging technologies in 2016, so it might be worth getting familiar with it. An open AI ecosystem refers to the idea that with an unprecedented amount of data available, combined with advances in natural language processing and social awareness algorithms, applications of AI will become increasingly more useful to consumers.
It is especially true in the case of medicine and healthcare. There is so much data to utilize: patient medical history records, treatment data – and lately information coming from wearable health trackers and sensors. This huge amount of data could be analyzed in details not only to provide patients who want to be proactive with better suggestions about lifestyle, but it could also serve healthcare with instructive pieces of information about how to design healthcare based on the needs and habits of patients.
Analyzing a healthcare system:
97% of healthcare invoices in the Netherlands are digital containing data regarding the treatment, the doctor and the hospital. These invoices could be easily retrieved. A local company, Zorgprisma Publiek analyzes the invoices and uses IBM Watson in the cloud to mine the data. They can tell if a doctor, clinic or hospital makes mistakes repetitively in treating a certain type of condition in order to help them improve and avoid unnecessary hospitalizations of patients.
What do we need to make these really happen?
First and foremost, we have to tear down the prejudices and fears regarding artificial intelligence and help the general population understand how AI could be beneficial and how we can fight its possible dangers. The biggest fear is that AI will become so sophisticated that it will work better than the human brain and after a while it will aim to take control over our lives. Stephen Hawking even said that the development of full artificial intelligence could spell the end of the human race. Elon Musk agreed.
I do not think that the situation is so gloomy, but I agree with those who stress the need to prepare for the use of artificial intelligence appropriately. We need the following preparations to avoid the pitfalls of the utilization of AI:
1. Creation of ethical standards which are applicable to and obligatory for the whole healthcare sector
2. Gradual development of AI in order to give some time for mapping of the possible downsides
3. For medical professionals: acquirement of basic knowledge about how AI works in a medical setting in order to understand how such solutions might help them in their everyday job
4. For patients: getting accustomed to artificial intelligence and discovering its benefits for themselves – e.g. with the help of Cognitoys which support th cognitive development of small children with the help of AI in a fun and gentle way or with such services as Siri.
5. For companies developing AI solutions (such as IBM): even more communication towards the general public about the potential advantages and risks of using AI in medicine.
6. For decision-makers at healthcare institutions: doing all the necessary steps to be able to measure the success and the effectiveness of the system. It is also important to push companies towards offering affordable AI-solutions, since it is the only way to bring the promise of science fiction into reality and turn AI into the stethoscope of the 21st century.
If we succeed, huge medical discoveries and treatment breakthroughs will dominate the news not from time to time, but several times a day. If you ever come across or use a narrow AI system, you will understand my optimism.