AI clinical

AI outperforms clinicians in triaging post-operative patients for ICUe.

Artificial intelligence correctly triaged 41 of the 50 patients in the study (82%). Surgeons had an accuracy triage rate of 70% (35 patients), intensivists 64% (32 patients), and anaesthesiologists 58% (29 patients). The number of incorrect triage decisions was lowest for AI (18%), followed by 30% for surgeons, 36% for intensivists, and 42% for anaesthesiologists.

Editor’s pick, (2019). AI outperforms clinicians in triaging post-operative patients for ICUe. Medical brief.

These are the kinds of contexts where we’ll increasingly see the use of machine learning algorithms to “provide guidance” to clinicians: high stakes decision-making scenarios where the correct outcome relies on the integration of data from a wide variety of clinical domains that are not optimised for human cognition. It’s just not possible for a human being – or team of human beings – to track the high number of relevant and inter-related variables that influence these kinds of clinical outcomes.

The resulting algorithm included 87 clinical variables and 15 specific criteria related to admission to the ICU within 48 hours of surgery.

By Michael Rowe

I'm a lecturer in the Department of Physiotherapy at the University of the Western Cape in Cape Town, South Africa. I'm interested in technology, education and healthcare and look for places where these things meet.