Deep learning relies on large, structured data sets. But when it comes to a complex medical diagnosis that involves many different factors, the number of patients for whom the complete set of data is available is often too limited to build a sufficiently reliable predictive algorithm .
Of course, as technology evolves and we manage to successfully address some of the data challenges in healthcare, AI’s ability to make meaningful predictions from complex and multimodal data will continue to grow.
Yet still, there may not always be prior examples for deep learning algorithms to build on. A patient may present with a unique combination of symptoms. A new drug may have unforeseen side effects. Such novel variations occur all the time in clinical practice; defying what can be algorithmically derived from historical data. Or as AI pioneer Gary Marcus puts it: “in a truly open-ended world, there will never be enough data .”
This raises even more fundamental questions about the complementary nature of AI and human expertise – and the future course of AI innovation we set for ourselves. In our excitement about the possibilities of deep learning, have we become too one-sidedly focused on big data as the fuel of all AI innovation? What about the rich body of clinical and scientific knowledge that we have already acquired over the ages? Can we find new and creative ways to bring together the best of both worlds?
These are questions I will explore in more detail in a future article, and I very much welcome your thoughts as well – because I truly believe there is never one single approach that renders all others useless or obsolete.
Medicine is complex. Intelligence is multifaceted. Only by acknowledging this complexity can we advance healthcare in a meaningful way – combining the best of what human experts and AI have to offer, and ultimately allowing them to deliver better patient care together.
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