The ability to predict in healthcare is the utopia promised by every artificial intelligence for healthcare built, funded and tested in the last decade. Yet very few doctors, technologists, or investors would have imagined they would live to witness a pandemic of the scale we are currently experiencing. We are still getting our heads round the lives lost, the lives of the frontline workers at risk, the disruption and self-isolation, the less fortunate who will suffer the most, the companies in survival mode, and a battered global economy. It is a good time to reflect on what the future of health will look like after we recover. We need to get better at acting on the predictions that truly matter. In a booming health-tech market saturated with promises of predictions and diagnostic insights, it’s a shame we didn’t listen to the scientists who predicted this violent wave of viral disruption.
The future of healthcare investing needs to change
With the first case of the virus last December, everything changed, and there is so much more change to come, in healthcare, technology and in the way we all work. Like with policy and public health, the majority of players on the healthcare stage remain so far removed from the frontline. The perceived ‘market’ rarely truly represents the real one, and true intelligence is lacking the collective intelligence that should prioritise the needs of the healthcare systems and the populations they serve. Our values, motives and how we create the pitch-perfect melting pot of skills, expertise, and mindset needs readjustment. Somewhere between evidence- based decision making and patience; clinical impact aligned with economic impact should be the goal. More focus is needed on validation and less on valuations that are largely built on assumptions and unproven hypotheses. Given the amount of investment that has drowned the healthtech/biotech domains in the last decade, we must praise the advancements that have been made. We must also examine the failures, the wasted resources, and whether technology really is moving healthcare forward at a pace that matches the investment.
AI in medical imaging entered the consciousness of radiologists just a few years ago, notably peaking in 2016 when Geoffrey Hinton declared radiologists’ time was up, swiftly followed by the first AI startups booking exhibiting booths at RSNA. Three years on, the sheer number and scale of AI-focussed offerings has gathered significant pace, so much so that this year a decision was made by the RSNA organising committee to move the ever-growing AI showcase to a new space located in the lower level of the North Hall. In some ways it made sense to offer a larger, dedicated show hall to this expanding field, and in others, not so much. With so many startups, wiggle room for booths was always going to be an issue, however integration of AI into the workflow was supposed to be a key theme this year, made distinctly futile by this purposeful and needless segregation.
By moving the location, the show hall for AI startups was made more difficult to find, with many vendors verbalising how their natural booth footfall was not as substantial as last year when AI was upstairs next to the big-boy OEM players. One witty critic quipped that the only way to find it was to ‘follow the smell of burning VC money, down to the basement’. Indeed, at a conference where the average step count for the week can easily hit 30 miles or over, adding in an extra few minutes walk may well have put some of the less fleet-of-foot off. Several startup CEOs told us that the clientele arriving at their booths were the dedicated few, firming up existing deals, rather than new potential customers seeking a glimpse of a utopian future. At a time when startups are desperate for traction, this could have a disastrous knock-on effect on this as-yet nascent industry.
It wasn’t just the added distance that caused concern, however. By placing the entire startup ecosystem in an underground bunker there was an overwhelming feeling that the RSNA conference had somehow buried the AI startups alive in an open grave. There were certainly a couple of tombstones on the show floor — wide open gaps where larger booths should have been, scaled back by companies double-checking their diminishing VC-funded runway. Zombie copycat booths from South Korea and China had also appeared, and to top it off, the very first booth you came across was none other than Deep Radiology, a company so ineptly marketed and indescribably mysterious, that entering the show hall felt like you’d entered some sort of twilight zone for AI, rather than the sparky, buzzing and upbeat showcase it was last year. It should now be clear to everyone who attended that Gartner’s hype curve has well and truly been swung, and we are swiftly heading into deep disillusionment.
Artificial intelligence requires data. Ideally that data should be clean, trustworthy and above all, accurate. Unfortunately, medical data is far from it. In fact medical data is sometimes so far removed from being clean, it’s positively dirty.
Consider the simple chest X-ray, the good old-fashioned posterior-anterior radiograph of the thorax. One of the longest standing radiological techniques in the medical diagnostic armoury, performed across the world by the billions. So many in fact, that radiologists struggle to keep up with the sheer volume, and sometimes forget to read the odd 23,000 of them. Oops.
Surely, such a popular, tried and tested medical test should provide great data for training AI? There’s clearly more than enough data to have a decent attempt, and the technique is so well standardised and robust that surely it’s just crying out for automation?Continue reading…