Collecting valuable data David Bishop

DeepMind, Google’s London-based artificial intelligence company, has started training neural networks to recognise the signs of eye disease in medical images. A partnership with Moorfields Eye Hospital in London has given the company access to about a million anonymised retinal scans, which DeepMind will feed into its artificial intelligence software.

The project will target two of the most common eye diseases – age related macular degeneration and diabetic retinopathy. More than 100 million people around the world have these conditions.

The information that Moorfields is providing includes scans of the back of people’s eyes, as well as more detailed scans known as optical coherence tomography (OCT). The idea is that the images will let DeepMind’s neural networks learn to recognise subtle signs of degenerating eye conditions that even trained clinicians have trouble spotting.


This could make it possible for a machine learning system to detect the onset of disease before a human doctor could. The earlier the better, says Gadi Wollstein, an eye doctor at the University of Pittsburgh. “Patients are losing tissue and the loss is irreversible,” he says. “The longer we’re waiting, the worse the [outcome].”

Data overload

In 2005, Wollstein and his colleagues explored the use of neural networks to diagnose eye disease. But the team had a far smaller data set than DeepMind has been given. Wollstein says that a large data set is critical, as it allows the neural network to learn to recognise eye disease more completely and accurately.

Ophthalmologists are using highly detailed OCT scans more and more. But this can lead to data overload. It’s often hard for a doctor to see clear patterns and make good diagnoses, says Wollstein. He thinks a machine might do a better job.

Any automated diagnosis software DeepMind comes up with could also make its way to high-street opticians, who are increasingly using OCT, says Pearse Keane, the Moorfields ophthalmologist who approached DeepMind with the idea in 2015. “About 10 per cent of high-street opticians have OCT– it’s likely that big national chains will adopt it.”

Share and share alike

A previous data-sharing agreement between DeepMind and the Royal Free NHS Trust raised questions about the need for regulatory and ethical approval. The full scale of that project only came to light after a freedom of information request from New Scientist.

In this case, however, DeepMind and Moorfields are open about the conditions of the agreement, laying out exactly how much and what kind of data they are using for the research.

The sharing of anonymised historical data has already been approved by Moorfields’ research and development office, and DeepMind says it has applied to the Health Research Authority for clearance to access more sensitive data. “This data sharing deal currently appears to have followed the rules,” says Phil Booth of medical privacy advocacy group MedConfidential.

DeepMind’s partnership with Moorfields also gives us an early look at how the marketplace for machine learning could work. DeepMind will not get paid for any of the work it does, whether with the Royal Free or Moorfields Eye Hospital. However, it does get to test out algorithms on real data sets that describe serious problems, and it gets to keep the neural networks it trains using that data.

The valuable knowledge about eye disease contained in Moorfield’s anonymous dataset will become the property of DeepMind, built into its artificial intelligence systems. In effect, training its machine learning systems on real-world health data is DeepMind’s payment for advancing the field of diagnostic AI.

Asking the right questions

Mustafa Suleyman, who leads DeepMind’s healthcare research team, points out that the project might not work out and the models it produces might be worthless. “It may be that we have to go back to the drawing board,” he says.

For Wollstein, who worked at Moorfields in the 1990s, the exchange is worth it. DeepMind may get free access to valuable patient data – but the alternative is to keep potential insight locked up in the Moorfields dataset, inaccessible to human analysis. At the end of the day, says Wollstein, DeepMind’s research may result in a great boost to the NHS, at zero financial cost to the taxpayer.

But some still have reservations about Google’s use of NHS data. “Everyone is assuming that Moorfields desperately needs some kind of unique insight from Google,” says Javier Ruiz of the Open Rights Group. “DeepMind are certainly capable when it comes to AI. But you could do machine learning on this data without DeepMind. You have to question whether that is the right quid pro quo,” he says.

By being open about their work on public data, DeepMind and Moorfields make it possible for such questions to be asked. There are no clear answers, but DeepMind’s research will play a key role in determining how the public thinks about the value locked up in its data.

“Ultimately the question is this: if you say you’re going to use publicly funded data, you need a very, very clear public benefit,” says Ruiz.