Researchers in Singapore have shown that advanced artificial intelligence (AI) techniques can significantly improve clinical diagnostics in countries with limited resources without the need for massive local datasets.
A team from Duke-NUS Medical School has successfully applied transfer learning, a method where a model developed for one task is reused as the starting point for another, to predict patient outcomes after cardiac arrest.
The study, published in npj Digital Medicine, addresses a common challenge in AI adoption in low- and middle-income countries, which is the lack of extensive, high-quality data required to train algorithmic models from scratch.
To test the effectiveness of transfer learning, the researchers used a brain-recovery prediction model originally built in Japan using data from 46,918…