Effectiveness of an Artificial Intelligence Software for Limb Radiographic Fracture Recognition in an Emergency Department
Objective:
Assessment of the impact of an Artificial Intelligence (AI) limb bone fracture diag-4 nosis software (AIS) on emergency department (ED) workflow and diagnostic accuracy.
Methods:
A retrospective study was conducted in two phases: without AIS (Period 1: January 1, 2020 - June 30, 2020) and with AIS (Period 2: January 1, 2021 - June 30, 2021).
Results:
Among 3720 patients (1780 in Period 1, 1940 in Period 2), the discrepancy rate decreased by 17% (p = 0.04) after AIS implementation. Clinically relevant discrepancies showed no significant change (-1.8%, p = 0.99). The mean length of stay in the ED was reduced by 9 minutes (p = 0.03), and expert consultation rates decreased by 1% (p = 0.38).
Conclusion:
AIS implementation reduced the overall discrepancy rate and slightly decreased ED length of stay, although its impact on clinically relevant discrepancies remains inconclusive.
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