Effectiveness of an Artificial Intelligence Software for Limb Radiographic Fracture Recognition in an Emergency Department

Abstract

<b>Objectives:</b> To assess the impact of an Artificial Intelligence (AI) limb bone fracture diagnosis software (AIS) on emergency department (ED) workflow and diagnostic accuracy. <b>Materials and Methods:</b> A retrospective study was conducted in two phases-without AIS (Period 1: 1 January 2020-30 June 2020) and with AIS (Period 2: 1 January 2021-30 June 2021). <b>Results:</b> Among 3720 patients (1780 in Period 1; 1940 in Period 2), the discrepancy rate decreased by 17% (<i>p</i> = 0.04) after AIS implementation. Clinically relevant discrepancies showed no significant change (-1.8%, <i>p</i> = 0.99). The mean length of stay in the ED was reduced by 9 min (<i>p</i> = 0.03), and expert consultation rates decreased by 1% (<i>p</i> = 0.38). <b>Conclusions:</b> AIS implementation reduced the overall discrepancy rate and slightly decreased ED length of stay, although its impact on clinically relevant discrepancies remains inconclusive. <b>Key Point:</b> After AI software deployment, the rate of radiographic discrepancies decreased by 17% (<i>p</i> = 0.04) but this was not clinically relevant (-2%, <i>p</i> = 0.99). Length of patient stay in the emergency department decreased by 5% with AI (<i>p</i> = 0.03). Bone fracture AI software is effective, but its effectiveness remains to be demonstrated.

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