Rachel Roumeliotis on the impact of AI on healthcare in the wake of COVID-19 and it’s potential for transforming the future of the industry.
AI is poised to significantly alter every aspect of healthcare. From personal health to clinical care and drug trials, AI could have a huge impact, but it will take careful planning and thoughtful implementation. Leadership will be key to making this transformation possible. Building a knowledge base, creating a roadmap of adoption, and instilling confidence throughout organisations will be key.
The current pandemic has opened everyone’s eyes to how quickly the healthcare sector needs to adapt to its surroundings. It has pushed healthcare systems to their limits, with hospitals under increasing pressure to treat patients with the utmost care. With the strain on healthcare systems still on the rise, researchers are rapidly looking for solutions. This is where technology, in particular AI and machine learning comes in. We must look at how AI is set to make a difference now when we most need it, as well as in the coming years.
AI in the age of COVID-19
From tracking the spread and predictive modelling of what might happen next, to racing towards a cure, COVID-19 has exemplified the important role that technology plays in the healthcare system,
At the start of the outbreak, an AI warning system developed by BlueDot used natural-language processing and machine learning, to track over 100 infectious diseases by analysing approximately 100,000 articles in 65 languages every day.
This helped spot an early warning about the outbreak, days before both the US Centers for Disease and Prevention and the World Health Organisations sent out official notices. In a recent blog post, BlueDot noted, “While diseases spread fast, knowledge can spread even faster”. It would have been near impossible do compile this amount of information and sift through it without the help of AI and critical data could have been missed.
While an AI system pulling all of this data might not be able to find the solution to the spread of the virus alone, in a data dense industry such as healthcare, it gets researchers a lot closer to an answer by working with it.
Data programming tools having the power to change medical diagnoses
Few industries are as data intensive as medicine. Medical data comes in many forms: images, audio, video, unstructured text and structured information. All this data suffers from the traditional problems experienced by other industries: missing information, corrupt values, suspicious outliers, lack of labelling, typographic errors and more.
As medical databases multiply, cleaning and labelling information is becoming ever more critical. Although we are some way from solving this challenge, we are seeing important progress with the likes of Holoclean and Snorkel. The former is an open-source, machine learning-based system for automatic error detections and repair which has been used successfully in several medical applications, including in hospitals.
Snorkel, meanwhile, is an open-source data programming school which automates the time-consuming task of creating training and programmatically labelling large data sets used for training machine learning applications. The technology has already seen significant success in the medical sector.
One project to classify rare aortic valve malformations used a huge, population-scale data set from the UK Biobank and, using data programming, was able to automate labelling for roughly 4,000 previously unlabelled MRI sequences – work that would otherwise have had to be done by hand.
The same data programming tools are also bringing success in biomedical image analysis, as well as extracting knowledge that’s buried within existing resources. For example, Snorkel’s developers have created a data extraction tool that combs through biomedical literature to extract associations between traits and genomic variants.
In this way, AI is furthering our medical knowledge while simultaneously delivering faster and more accurate diagnoses – an especially important consideration given the shortfall in highly-trained medical staff.
Taking care of your personal information
One of the defining data challenges in the medical industry is the incredibly sensitive nature of the information. Not only are we dealing with people’s personal medical history, but pharma and other medical businesses naturally keep their data guarded. However, great leaps forward require us to pool this data together to find the insights that lead to a better understanding of diseases and improved treatments.
At a recent Artificial Intelligence conference in Beijing, Ion Stoica, director of UC Berkeley’s RISELab, described new projects that enable organisations to cooperate without actually sharing data. This new collaboration model has been dubbed “coopetition” – collecting anonymised data to create a global model base that every participant can use for their own projects.
What’s especially exciting about coopetitive learning is that it has applications in other industries with enormous data sets of sensitive data. Financial institutions, for example, can use this model to build more accurate and powerful fraud models, showing how the technologies being pioneered in medicine will soon be delivering improvements in other areas of our lives.
New markets developing in the industry
We must note that this model is one example of the new markets being developed across the whole of the healthcare industry. For example, one company that is creating technology to equip for the future is Computable Labs, a startup that is building tools to create these new data markets, addressing important issues such as governance of the market, ascribing value to data within the market, and protocols for ensuring privacy.
Next is RISELab, which is taking the idea a step further by envisioning new kinds of two-sided markets, which are mediated on both sides by AI.
To give just one example of how this can work, imagine that you’re a diabetic and using a service that recommends recipes based on your condition – but you don’t like a lot of the dishes that the service recommends. In a two-sided, AI-mediated market, your recommendation engine would understand your tastes and needs, and then communicate with the other engine to negotiate a satisfactory menu.
However, developing new market mechanisms that are built on top of data flows won’t only have a major impact on the medical industry; it actually represents an incredibly ambitious attempt to reimagine the inner workings of capitalism itself.
While the data-intensive healthcare sector is an obvious place for this to emerge, the applications are potentially limitless. It’s just one example of how medicine is not just helping us live long enough to enjoy a better, more technically-advanced future, but actually helping to create it too.
What the future holds
What the pandemic has shown us, is how humans and AI can work together to adapt quickly. Whether AI has a larger role to play in finding a drug to cure COVID-19 is still yet to be seen, but we must be optimistic that AI will support organisations in the fight to find a cure and will have a large role to play in the future of the industry.
Rachel Roumeliotis is vice president of Data and AI at O’Reilly.
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