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Artificial Intelligence in Healthcare: Babylon Health & IBM Watson Take the Lead
A person becomes a doctor by graduating from medical school, but becomes a good doctor only after years of experience. What if we could give doctors the intuition that comes from years of experience on their first day? Several companies believe they can help every doctor achieve the same outcomes as top experts with tools powered by artificial intelligence (AI). AI is a term used for computer programs that can do things that we think of as “human”. Scientists use AI to automate complex tasks like driving a car or stock trading. AI has also been used in the development of new drugs.
AI for the patient and provider:
Babylon Health wants everyone with a smartphone to have access to affordable healthcare. They believe an app that offers instant diagnosis is the key. As their CEO, Ali Parsa, explained to the Telegraph:
“[Medical professionals] are the most expensive part of healthcare. And the second… is timing… [By] the time [most diseases] present their symptoms a £10 problem has become a £1,000 solution.”
Babylon Health believes they can drop both of those costs. Today, Babylon Health offers a free app that makes it simple for users to track their health and consult their AI-powered chatbot. For a fee, users can video-chat with top doctors who can access that user’s health records and a set of proprietary AI-powered tools that Babylon Health claims can improve treatment quality. By tracking the vitals, treatments, and outcomes across a broad user base, Babylon health has tapped an incredibly valuable dataset. This dataset makes it scalable to continually improve their AI’s performance alongside customers’ health.
IBM Watson for Oncology has a narrower focus: improving the outcomes of cancer treatments. IBM believes they can give every medical professional treating cancer the same insight that doctors at top cancer research centers have. IBM has partnered with specialists at Memorial Sloan Kettering to train their computers with a wealth of medical records and research. Launched in 2016, Watson supports doctors with patient-specific recommendations from cutting-edge treatments in a fraction of the time. According to Deborah DiSanzo, the General Manager of IBM Watson Health, Watson for Oncology had already been used in the treatment of 16,000 patients by the third quarter of 2017. With computers handling the analysis, doctors can focus on what humans excel at: treating the emotional distress of a patient fighting cancer.
Data for artificial intelligence is food for thought:
Both IBM Watson and Babylon Health agree: doctors can deliver better treatment by learning from the results of other patients. AI can learn from historical data and forecast how a patient’s ailment would respond to treatment options. Both companies are using machine learning, a technique that has become synonymous with AI in recent years. Machine learning is an automated technique used by a computer to teach itself to make decisions using training data. Training data is the fuel of machine learning, as described by Andrew Ng of Stanford University.
Babylon Health and IBM Watson have both designed systems that generate this “fuel” from their users. As they attract more users, they will generate better insights. This network effect is a virtuous circle where the product becomes better as it adds more users. The downside of products with network effects is that they are notoriously difficult to kick-start. Just think how hard it is to get the first few members for a dating website.
Babylon Health and IBM Watson have each partnered with established players to overcome this challenge and get the fuel they need to prime the motor. Babylon Health is bootstrapping their product with help from a UK NHS partnership. The UK NHS is seeking ways to mitigate their doctor shortage, and will trial Babylon’s chatbot for six months in North Central London, an area covering 1.2 million citizens. IBM Watson is partnering with Memorial Sloan Kettering to help train Watson on the wealth of clinical information and medical expertise that the center is known for.
Regulatory risk: A potential challenge:
With AI-powered healthcare products showing so much promise, one might expect regulation to pass quickly through the FDA. However, the FDA is currently struggling. As the Wall Street Journal puts it:
“How on earth are you going to regulate software that learns?”
Current regulations lack standards to assess the safety and efficacy of AI systems, which the FDA has attempted to address by providing guidance for assessing AI systems. The first guidance classifies AI systems as “general wellness products”, which are loosely regulated as they present low risk to users. The second guidance justifies the use of real-world evidence to assess the performance of AI systems. Lastly, the guidance clarifies the rules for the adaptive design in clinical trials, which would be widely used in assessing the operating characteristics of AI systems.
Despite these challenges, things are looking bullish for AI-powered healthcare. Babylon Health and IBM are only two of many new projects that are extending the reach of healthcare by reinforcing the parts that don’t scale: doctors. While each of these companies has their own view of the future, they all agree that AI will let our limited medical professionals bring the best treatments to the greatest number of people. Especially when the best treatment is taking action before we get sick.
This article was coauthored by Salma Buddaseth:
Salma Buddaseth is a biomedical research scientist involved in pre-clinical drug development of small molecules and immunotherapeutics at Memorial Sloan Kettering Cancer Center, New York. She obtained her PhD in Biochemistry from Hannover Medical School, Germany, where she worked on cancer therapeutics and genetic mutations that influence susceptibility to drugs. Besides science and technology, she is passionate about entrepreneurship and business development. Salma’s other articles include Artificial Intelligence Meets Drug Development
Ben Pelcyger works at Comcast Advanced Advertising as Director of Business Analytics where he cultivates skills, technology, and processes to make data-informed decisions feel second nature. He is also the co-creator of a recurrent neural network that asks "Would You Rather..." questions on Twitter and the creator of a board game about online advertising. While he doesn’t excel at writing descriptions of himself, he does excel at terrible puns and data scheming (which is a terrible pun).