TY - JOUR

T1 - The added effect of artificial intelligence on physicians’ performance in detecting thoracic pathologies on CT and chest X-ray

T2 - A systematic review

AU - Li, Dana

AU - Pehrson, Lea Marie

AU - Lauridsen, Carsten Ammitzbøl

AU - Tøttrup, Lea

AU - Fraccaro, Marco

AU - Elliott, Desmond

AU - Zając, Hubert Dariusz

AU - Darkner, Sune

AU - Carlsen, Jonathan Frederik

AU - Nielsen, Michael Bachmann

N1 - Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2021

Y1 - 2021

N2 - Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation.

AB - Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation.

KW - Artificial intelligence

KW - Chest X-ray

KW - Computer-based devices

KW - CT

KW - Deep learning

KW - Observer tests

KW - Performance

KW - Radiology

KW - Thoracic diagnostic imaging

U2 - 10.3390/diagnostics11122206

DO - 10.3390/diagnostics11122206

M3 - Review

C2 - 34943442

AN - SCOPUS:85120303438

VL - 11

JO - Diagnostics

JF - Diagnostics

SN - 2075-4418

IS - 12

M1 - 2206

ER -