DeepMind, the Google-owned U.K. AI research firm, has published a research letter in the journal Nature in which it discusses the performance of a deep learning model for continuously predicting the future likelihood of a patient developing a life-threatening condition called acute kidney injury (AKI).

The company says its model is able to accurately predict that a patient will develop AKI “within a clinically actionable window” up to 48 hours in advance.

In a blog post trumpeting the research, DeepMind couches it as a breakthrough — saying the paper demonstrates artificial intelligence can predict “one of the leading causes of avoidable patient harm” up to two days before it happens.

“This is our team’s biggest healthcare research breakthrough to date,” it adds, “demonstrating the ability to not only spot deterioration more effectively, but actually predict it before it happens.”

Even a surface read of the paper raises some major caveats, though.

Not least that the data used to train the model skews overwhelmingly male: 93.6%. This is because DeepMind’s AI was trained using patient data provided by the U.S. Department of Veteran Affairs (VA).

The research paper states that females comprised just 6.38% of patients in the training data set. “Model performance was lower for this demographic,” it notes, without saying how much lower.

A summary of data set statistics also included in the paper indicates that 18.9% of patients were black, although there is no breakout for the proportion of black women in the training data set. (Logic suggests it’s likely to be less than 6.38%.) No other ethnicities are broken out.

Asked about the model’s performance capabilities across genders and different ethnicities, a DeepMind spokeswoman told us: “In women, it predicted 44.8% of all AKI early, in men 56%, for those patients where gender was known. The model performance was higher on African American patients — 60.4% of AKIs detected early compared to 54.1% for all other ethnicities in aggregate.”

“This research is just the first step,” she confirmed. “For the model to be applicable to a general population, future research is needed, using a more representative sample of the general population in the data that the model is derived from.

“The data set is representative of the VA population, and we acknowledge that this sample is not representative of the U.S. population. As with all deep learning models it would need further, representative data from other sources before being used more widely.

“Our next step would be to work closely with [the VA] to safely validate the model through retrospective and prospective observational studies, before hopefully exploring how we might conduct a prospective interventional study to understand how the prediction might impact care outcomes in a clinical setting.”

“To do this kind of work, we need the right kind of data,” she added. “The VA uses the same EHR [electronic health records] system (widely recognized as one of the most comprehensive EHRs) in all its hospitals and sites, which means the data set is also very comprehensive, clean, and well-structured.”

So what DeepMind’s ‘breakthrough’ research paper neatly underlines is the reflective relationship between AI outputs and training inputs.

In a healthcare setting, where instructive outputs could be the difference between life and death, it’s not the technology that’s king; it’s access to representative data sets that’s key — that’s where the real value lies.

This suggests there’s huge opportunity for countries with taxpayer-funded public healthcare systems to structure and unlock the value contained in medical data they hold on their populations to develop their own publicly owned healthcare AIs.

Indeed, that was one of the recommendations of a 2017 industrial strategy review of the U.K.’s life sciences sector.

Oxford University’s Sir John Bell, who led the review, summed it up in comments to The Guardian newspaper, when he said: “Most of the value is the data. The worst thing we could do is give it away for free.”

Streams app evaluation

DeepMind has also been working with healthcare data in the U.K.

Reducing the time it takes for clinicians to identify when a patient develops AKI has been the focus of an app development project it’s been involved with since 2015 — co-developing an alert and clinical task management app with doctors working for the country’s National Health Service (NHS).

That app, called Streams, which makes use of an NHS algorithm for detecting AKI, has been deployed in several NHS hospitals. And, also today, DeepMind and its app development partner NHS trust are releasing an evaluation of Streams’ performance, led by University College London.

The results of the evaluation have been published in two papers, in the Nature Digital Medicine and the Journal of Medical Internet Research.

In its blog DeepMind claims the evaluations show the​ ​app​ “​improved​ ​the​ ​quality​ ​of​ ​care​ ​for​ ​ patients​ ​by​ ​speeding​ ​up​ ​detection​ ​and​ ​preventing​ ​missed​ ​cases”, further claiming ​clinicians​ ​”were​ ​able​ ​to​ ​respond​ ​to​ ​urgent​ ​AKI​ ​cases​ ​in​ ​14​ ​minutes​ ​or​ ​less” — and suggesting that ​using​ ​existing​ ​systems​ “​might​ ​otherwise​ ​have​ ​taken​ ​many​ ​hours”.​ ​

It also claims a reduction in the cost of care to the NHS — ​from​ ​£11,772​ ​to​ ​£9,761​ ​for​ ​a hospital​ ​admission​ ​for​ ​a​ ​patient​ ​with​ ​AKI.​ ​

Though it’s worth emphasizing that under its current contracts with NHS trusts DeepMind provides the Streams service for free. So any cost reduction claims also come with some major caveats.

Simply put: We don’t know the future costs of data-driven, digitally delivered healthcare services — because the business models haven’t been defined yet. (Although DeepMind has previously suggested pricing could be based on clinical outcomes.)