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

To assess the impact of an Artificial Intelligence (AI) limb bone fracture diagnosis software (AIS) on emergency department (ED) workflow and diagnostic accuracy. 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 2...

Full description

Saved in:
Bibliographic Details
Published inJournal of clinical medicine Vol. 13; no. 18; p. 5575
Main Authors Herpe, Guillaume, Nelken, Helena, Vendeuvre, Tanguy, Guenezan, Jeremy, Giraud, Clement, Mimoz, Olivier, Feydy, Antoine, Tasu, Jean-Pierre, Guillevin, Rémy
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 20.09.2024
MDPI
Subjects
Accuracy
Artificial intelligence
Diagnosis
Diagnosis, Radioscopic
Emergency medical care
Fractures
Hospitals
Medical errors
Medical imaging
Orthopedics
Patients
Performance evaluation
Physicians
Reading
Software
Statistical analysis
Surgeons
Technology application
Trauma
France
workflow
radiology
AI
bone
emergency department
artificial intelligence
fractures
retrospective study
Online AccessGet full text

Cover

More Information
Summary:To assess the impact of an Artificial Intelligence (AI) limb bone fracture diagnosis software (AIS) on emergency department (ED) workflow and diagnostic accuracy. 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). Among 3720 patients (1780 in Period 1; 1940 in Period 2), the discrepancy rate decreased by 17% ( = 0.04) after AIS implementation. Clinically relevant discrepancies showed no significant change (-1.8%, = 0.99). The mean length of stay in the ED was reduced by 9 min ( = 0.03), and expert consultation rates decreased by 1% ( = 0.38). AIS implementation reduced the overall discrepancy rate and slightly decreased ED length of stay, although its impact on clinically relevant discrepancies remains inconclusive. After AI software deployment, the rate of radiographic discrepancies decreased by 17% ( = 0.04) but this was not clinically relevant (-2%, = 0.99). Length of patient stay in the emergency department decreased by 5% with AI ( = 0.03). Bone fracture AI software is effective, but its effectiveness remains to be demonstrated.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm13185575