TY - JOUR

T1 - Impact of Concurrent Use of Artificial Intelligence Tools on Radiologists Reading Time

T2 - A Prospective Feasibility Study

AU - Müller, Felix C.

AU - Raaschou, Henriette

AU - Akhtar, Naurien

AU - Brejnebøl, Mathias

AU - Collatz, Lene

AU - Andersen, Michael Brun

N1 - Publisher Copyright: © 2021 The Association of University Radiologists

PY - 2022

Y1 - 2022

N2 - Rational and Objectives: This study investigated how an AI tool impacted radiologists reading time for non-contrast chest CT exams. Materials and Methods: An AI tool was implemented into the PACS reading workflow of non-contrast chest CT exams between April and May 2020. The reading time was recorded for one CONSULTANT RADIOLOGIST and one RADIOLOGY RESIDENT by an external observer. After each case radiologists answered questions regarding additional findings and perceived case overview. Reading times were recorded for 25 cases without and 20 cases with AI tool assistance for each reader. Differences in reading time with and without the AI tool were assessed using Welch's t-test for non-inferiority with non-inferiority limits defined as 100 seconds for the consultant and 200 seconds for the resident. Results: The mean reading time for the radiology resident was not significantly affected by the AI tool (without AI 370s vs with AI 437s; +67s 95% CI -28s to +163s, p = 0.16). The reading time for the radiology consultant was also not significantly affected by the AI tool (without AI 366s vs with AI 380s; +13s (95% CI - -57s to 84s, p = 0.70). The AI tool led to additional actionable findings in 5/40 (12.5%) studies and better overview in 18/20 (90%) of studies for the resident. Conclusion: A PACS based implementation of an AI tool for concurrent reading of chest CT exams did not increase reading time with additional actionable findings made as well as a perceived better case overview for the radiology resident.

AB - Rational and Objectives: This study investigated how an AI tool impacted radiologists reading time for non-contrast chest CT exams. Materials and Methods: An AI tool was implemented into the PACS reading workflow of non-contrast chest CT exams between April and May 2020. The reading time was recorded for one CONSULTANT RADIOLOGIST and one RADIOLOGY RESIDENT by an external observer. After each case radiologists answered questions regarding additional findings and perceived case overview. Reading times were recorded for 25 cases without and 20 cases with AI tool assistance for each reader. Differences in reading time with and without the AI tool were assessed using Welch's t-test for non-inferiority with non-inferiority limits defined as 100 seconds for the consultant and 200 seconds for the resident. Results: The mean reading time for the radiology resident was not significantly affected by the AI tool (without AI 370s vs with AI 437s; +67s 95% CI -28s to +163s, p = 0.16). The reading time for the radiology consultant was also not significantly affected by the AI tool (without AI 366s vs with AI 380s; +13s (95% CI - -57s to 84s, p = 0.70). The AI tool led to additional actionable findings in 5/40 (12.5%) studies and better overview in 18/20 (90%) of studies for the resident. Conclusion: A PACS based implementation of an AI tool for concurrent reading of chest CT exams did not increase reading time with additional actionable findings made as well as a perceived better case overview for the radiology resident.

KW - Computer-Assisted

KW - Image Interpretation

KW - Multidetector Computed Tomography

KW - Radiology

KW - Work Performance

U2 - 10.1016/j.acra.2021.10.008

DO - 10.1016/j.acra.2021.10.008

M3 - Journal article

C2 - 34801345

AN - SCOPUS:85119350641

VL - 29

SP - 1085

EP - 1090

JO - Academic Radiology

JF - Academic Radiology

SN - 1076-6332

IS - 7

ER -