YUHSpace

BROWSE

Export
0 0

Cited 0 times in

Cited 0 times in

The Impact of Artificial Intelligence on Radiologists' Reading Time in Bone Age Radiograph Assessment: A Preliminary Retrospective Observational Study

Authors
 Sejin Jeong  ;  Kyunghwa Han  ;  Yaeseul Kang  ;  Eun-Kyung Kim  ;  Kyungchul Song  ;  Shreyas Vasanawala  ;  Hyun Joo Shin 
Citation
 JOURNAL OF IMAGING INFORMATICS IN MEDICINE, Vol.38(4) : 1915-1923, 2025-08 
Journal Title
JOURNAL OF IMAGING INFORMATICS IN MEDICINE
ISSN
 2948-2925 
Issue Date
2025-08
MeSH
1
Keywords
Adolescent ; Age Determination by Skeleton* / methods ; Artificial Intelligence* ; Child ; Female ; Humans ; Male ; Radiologists* ; Retrospective Studies ; Time Factors
Abstract
Artificial intelligence; Bone age measurement; Radiography; Radiologists; Time
Article Number
 10.1007/s10278-024-01323-3 
DOI
To evaluate the real-world impact of artificial intelligence (AI) on radiologists' reading time during bone age (BA) radiograph assessments. Patients (<19 year-old) who underwent left-hand BA radiographs between December 2021 and October 2023 were retrospectively included. A commercial AI software was installed from October 2022. Radiologists' reading times, automatically recorded in the PACS log, were compared between the AI-unaided and AI-aided periods using linear regression tests and factors affecting reading time were identified. A total of 3643 radiographs (M:F=1295:2348, mean age 9.12 ± 2.31 years) were included and read by three radiologists, with 2937 radiographs (80.6%) in the AI-aided period. Overall reading times were significantly shorter in the AI-aided period compared to the AI-unaided period (mean 17.2 ± 12.9 seconds vs. mean 22.3 ± 14.7 seconds, p < 0.001). Staff reading times significantly decreased in the AI-aided period (mean 15.9 ± 11.4 seconds vs. mean 19.9 ± 13.4 seconds, p < 0.001), while resident reading times increased (mean 38.3 ± 16.4 seconds vs. 33.6 ± 15.3 seconds, p = 0.013). The use of AI and years of experience in radiology were significant factors affecting reading time (all, p≤0.001). The degree of decrease in reading time as experience increased was larger when utilizing AI (-1.151 for AI-unaided, -1.866 for AI-aided, difference =-0.715, p<0.001). In terms of AI exposure time, the staff's reading time decreased by 0.62 seconds per month (standard error 0.07, p<0.001) during the AI-aided period. The reading time of radiologists for BA assessment was influenced by AI. The time-saving effect of utilizing AI became more pronounced as the radiologists' experience and AI exposure time increased.
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Eun-Kyung(김은경) ORCID logo https://orcid.org/0000-0002-3368-5013
Shin, Hyun Joo(신현주) ORCID logo https://orcid.org/0000-0002-7462-2609
Han, Kyung Hwa(한경화)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/207418
사서에게 알리기
  feedback
Show full item record Find it @ YMLIB

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

  • License
    sherpa_romeo
    sherpa_juliet

OAK

DSpace Software Copyright © 2002-2017 Duraspace Yonsei University Medical Library All right Reserves. / TEL:02-2228-2915 /
YUHSpace는 국립중앙도서관 OAK 보급사업으로 구축되었습니다.

Browse

Links