Appraising the value of AI in wrist fracture detection: A clinical review

Artificial intelligence (AI) has become increasingly tested for improving fracture detection. This scoping review evaluates how AI can improve human performance in detecting traumatic wrist fractures.

A scoping review was conducted to identify studies comparing AI and human performance for wrist fracture detection. Nine resources were searched, including key databases Embase, MEDLINE, and SCOPUS. Studies which did not include AI being used as an assistant were excluded.

Nine studies were included in the review, where AI models demonstrated sensitivity from 83% to 97.7%, specificity from 77% to 96%, negative predictive value (NPV) from 89% to 90%, and positive predictive value (PPV) from 83% to 92%. Human performance demonstrated sensitivity from 58% to 94%, specificity from 77% to 97%, a NPV from 62% to 87%, and a PPV from 74% to 93%. When AI was used as a diagnostic aid, combined performance generally improved across all studies. Sensitivity gains ranged from 3% to 19%, with greater improvements among less experienced readers. Specificity changed between -4% to 11% when AI was used as a diagnostic aid. NPV and PPV changed from 6% to 13% and -5% to 7% when AI was used as a diagnostic aid, respectively.

Models using AI improved sensitivity and negative predictive value across all readers, while some readers had a reduced specificity or positive predictive value when using AI. Overall, AI has demonstrated promising results in traumatic wrist fracture detection when used as an assistant to support diagnosis.