Complete skeptics concerning AI in healthcare are likely few and far between, but that doesn’t mean there isn’t room for healthy caution when it comes to the numerous hurdles still to be leapt as healthcare systems implement new AI technology.
According to reports, there was no shortage of such caution articulated at the recent Intelligent Health conference in Basel, Switzerland.
For example, in a panel discussion, Stephanie Kuku, senior research fellow at University College Hospital London, challenged tech companies to, in essence, “show us the goods” when it comes to promising dramatic differences in healthcare outcomes due to the introduction of AI.
For one thing, she noted, new AI and relevant healthcare infrastructure go hand in hand.
“You’ve got to have the infrastructure to make sure patients get the care they need,” she argued.
“There’s no point having a tool that tells you that you need to go to hospital if you can’t get to one. There are a lot of examples, sadly, of tools that are not making a difference to patient outcomes.”
Moreover, in what has become a widespread concern, Kuku argued that AI algorithms needed to be trained using demographically accurate data.
“If you’re going to sell algorithms to NHS, you need to make sure data is representative,” she said. “The more we can generalize these algorithms, the more we can think about how it can help as many people as possible. Every team where you built models needs to be intensely multi-disciplinary. The more multi-disciplined your team is, the better, safer and more successful you’ll be in terms of both patient and financial outcomes.”
With similar concerns, Charles Alessi, chief clinical officer at HIMSS International, suggested a number of potentially beneficial new technologies were essentially failing as a result of “botched” deployments.
“I’ve seen wonderful technology introduced into healthcare systems all over the world, followed soon after by the death of these wonderful new technologies, because people don’t think about how to deploy them,” he lamented. “We need to think: how does it influence a business model, or a doctor’s consultation? You remove one brick and suddenly the whole thing falls apart. What you tend to get is unexpected consequences, with boring regularity.”
Yet another concern was raised by Karl Goossens, associate partner at data analytics firm QuantumBlack: the need to get the right talent in place to make the best of new AI.
“You might have a great data model and great output, but a lot of people aren’t analytic-savvy,” explained Goossens, adding that presenting data insights in understandable forms is critical when it comes to demonstrating AI’s benefits to both healthcare professionals and the public.