An objective framework for evaluating unrecognized bias in medical AI models predicting COVID-19 outcomes.

ABSTRACT:

Objective

The increasing translation of artificial intelligence (AI)/machine learning (ML) models into clinical practice brings an increased risk of direct harm from modeling bias; however, bias remains incompletely measured in many medical AI applications. This article aims to provide a framework for objective evaluation of medical AI from multiple aspects, focusing on binary classification models.

Materials and methods

Using data from over 56 000 Mass General Brigham (MGB) patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we evaluate unrecognized bias in 4 AI models developed during the early months of the pandemic in Boston, Massachusetts that predict risks of hospital admission, ICU admission, mechanical ventilation, and death after a

SUBMITTER: Estiri H 

PROVIDER: S-EPMC9277645 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature