Karsten Lemm, guest author
Signs of the drama that is about to unfold begin to appear almost 30 minutes beforehand. But no one takes any notice, because the monitoring devices don’t sound the alarm until it’s too late. Suddenly, seemingly out of nowhere, the patient experiences a circulatory collapse, and the doctors rush in to resuscitate them.
“In the intensive care unit, we currently work reactively,” says Alexander Meyer, a professor at the German Heart Centre Berlin. Only when the body is in dire need “do the traditional alarms go off. But by then it’s already too late.”
For Meyer, who also has a degree in computer science, the solution to this problem lies in artificial intelligence (AI). These algorithms that learn as they are applied can be trained to recognise patterns in values that are measured in order to react to even the smallest deviations; deviations that signal early on that important bodily functions are impaired, that a collapse is possibly imminent.
Until a few years ago, this system was still just a dream. But Meyer has since developed it himself: x-cardiac is the start-up aimed at saving lives whilst reducing pressure on medical personnel. His AI has already been approved for predicting post-operative bleeding after heart surgery.
Around the world, start-ups like x-cardiac are fuelling the hope that adaptive computer systems can significantly improve medical care for billions of people. The spectrum of possible applications ranges from automated image analysis for tumour detection, drug development and AI-supported telemedicine, to optimising processes in hospitals.
“Artificial intelligence offers an opportunity to dramatically improve patient care, early detection of disease and efficiency at hospitals,” says Mariam Kremer, a partner at Global Founders Capital with a focus on artificial intelligence.
Investors are showing particular interest in the potential of AI in medical imaging and drug development. According to market researcher Signify Research, more than 13 billion dollars in start-up capital have flowed into these two areas to date. The lion’s share (10.7 billion dollars) went to start-ups specialising in solutions for the pharmaceutical industry.
Even though each of these companies is pursuing its own approach, the primary goal is usually to identify new active ingredients and develop more effective drugs. And to do so far more quickly than has been possible to date. “The ever-increasing capabilities of artificial intelligence are helping us find molecular compounds that are essential for drugs,” explains Kremer.
The power of this approach is exemplified by the Covid Moonshot project, which aims to swiftly develop a “pill that cures Covid”. In March 2020, the start-up PostEra called on pharmacologists around the world to submit suggestions for potential drug combinations that they would then assess using AI to identify promising candidates. Hundreds of scientists, as well as universities and pharmaceutical companies, are now participating in the open-source project.
“We started this initiative to develop an antiviral drug – without patents, without intellectual property, without profit,” says the CEO of PostEra, Aaron Morris. The AI helped in two ways: it sped up the process of identifying promising chemical compounds, reducing it from weeks to just a few hours, and it subsequently helped the researchers to construct the recipes to make the desired molecules. The second step in particular is essential, Morris explains: “Of course, the design is very important. But you then have to produce the molecules by synthesis and then test them – that’s the real challenge.”
With its AI system, PostEra aims to automate this difficult step in order to make drug development more predictable. Because until now, the risk for pharmaceutical companies has been enormous: around 90 percent of all projects fail, often after years of research and huge investments. On average, it costs 1.3 billion dollars to develop a new drug from the laboratory to its market launch.
“Radiology is an area of medicine where AI has started to have a positive impact,” says Sanjay Parekh, an expert on AI in imaging at Signify Research. According to Parekh, the systems have proven particularly useful as digital assistants that support people and make them more productive. “The benefit of AI is to help doctors get to a diagnosis quicker and more accurately by automating time-consuming tasks.”
Parekh doubts that AI alone would be able to take over the role of a doctor and replace humans any time soon. “Medicine is too complex of a field to have an AI algorithm come up with a final diagnosis,” he argues. Even small changes are enough to throw the algorithms off track and produce misdiagnoses.
In addition, there are still many areas where there is no reliable data available to train the digital assistants. With the exception of radiology, which produces plenty of image material, developers have a hard time compiling information and preparing it in a way that it can be used by a computer. “Health data today is still very untidy. It’s hard to access and unstructured. Making the data easily readable by an algorithm takes a lot of time and effort,” he says.
Details of this kind create obstacles for researchers trying to translate solutions from the lab to the real world. Success requires not only capital and expertise, but also a lot of patience. Because unlike e-commerce, delivery services or social media, every product must be certified by the authorities. After all, the technology is supposed to save lives – not endanger them.
“Our current healthcare system is set up to be far too reactive,” Kilian Koepsell, founder of Caption Health, whose mission is to detect disease early through ultrasound says. “There are a lot of serious illnesses that could actually be prevented and that incur unnecessary costs.” Why, Koepsell asks, shouldn’t it be possible to protect the body better – like in the case of cars? “When it comes to our vehicles, we don’t wait for something to start rattling; we have them checked regularly and comprehensively.”
In the meantime, it’s reassuring to know that AI can help with the age-old task of keeping our bodies healthy.
This article is an abridged version of an article that appeared in MAG/NET.
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