AI imaging bias reduces diagnostic accuracy

Systematically biased artificial intelligence (AI) imaging models lower diagnostic accuracy by over 11 percentage points, according to research published December 19 in JAMA. A team led by Sarah Jabbour from the University of Michigan in Ann Arbor also found that biased AI model predictions with explanations lowered accuracy by about nine percentage points. However, accuracy improved by over four percentage points when clinicians reviewed a patient clinical vignette with standard AI model predictions and model explanations compared with baseline measures. “Given the unprecedented pace of AI development, it is essential to carefully test AI integration into clinical workflows,” the Jabbour team wrote.  While AI continues to show its potential in aiding radiologists and other clinicians to diagnose patients, systematic bias persists as a barrier to the technology’s widespread use by reducing diagnostic accuracy. Recent regulatory guidance has called for AI models to include explanations to help lessen errors made by models. However, the researchers noted a lack of data showing the effectiveness of this strategy. Jabbour and co-authors studied the impact of systematically biased AI on clinician diagnostic accuracy, as well as whether image-based AI model explanations could decrease model errors. The multicenter study included hospitals in 13 U.S. states, using a survey administered between April 2022 and January 2023.  The team showed 572 participating clinicians nine...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Artificial Intelligence Source Type: news