Singapore, Jan. 31 -- After a cardiac arrest, families and doctors are often faced with agonising uncertainty about a patient's chances of recovery. This uncertainty is even greater in hospitals with limited resources, where access to advanced diagnostic tools and large datasets is constrained.

In one example of how artificial intelligence (AI) can help bridge this gap, researchers from Duke-NUS Medical School, Singapore and their collaborators have adapted an advanced AI model to accurately predict neurological recovery after cardiac arrest in a resource-limited setting.

Published innpj Digital Medicine,the study applied transfer learning, an advanced AI approach that adapts pre-trained models built on large datasets, to new settings w...