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Review
. 2023 May 18;12(10):3536.
doi: 10.3390/jcm12103536.

The Effects of Artificial Intelligence Assistance on the Radiologists' Assessment of Lung Nodules on CT Scans: A Systematic Review

Lotte J S Ewals  1   2 , Kasper van der Wulp  1 , Ben E E M van den Borne  3 , Jon R Pluyter  4 , Igor Jacobs  5 , Dimitrios Mavroeidis  6 , Fons van der Sommen  2 , Joost Nederend  1
Affiliations
Review

The Effects of Artificial Intelligence Assistance on the Radiologists' Assessment of Lung Nodules on CT Scans: A Systematic Review

Lotte J S Ewals et al. J Clin Med. .

Abstract

To reduce the number of missed or misdiagnosed lung nodules on CT scans by radiologists, many Artificial Intelligence (AI) algorithms have been developed. Some algorithms are currently being implemented in clinical practice, but the question is whether radiologists and patients really benefit from the use of these novel tools. This study aimed to review how AI assistance for lung nodule assessment on CT scans affects the performances of radiologists. We searched for studies that evaluated radiologists' performances in the detection or malignancy prediction of lung nodules with and without AI assistance. Concerning detection, radiologists achieved with AI assistance a higher sensitivity and AUC, while the specificity was slightly lower. Concerning malignancy prediction, radiologists achieved with AI assistance generally a higher sensitivity, specificity and AUC. The radiologists' workflows of using the AI assistance were often only described in limited detail in the papers. As recent studies showed improved performances of radiologists with AI assistance, AI assistance for lung nodule assessment holds great promise. To achieve added value of AI tools for lung nodule assessment in clinical practice, more research is required on the clinical validation of AI tools, impact on follow-up recommendations and ways of using AI tools.

Keywords: X-ray computed; artificial intelligence; computer-assisted; detection; diagnosis; lung neoplasms; malignancy prediction; systematic review; tomography.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
PRISMA flow diagram of the search for eligible studies.
Figure 2
Figure 2
Summarized QUADAS-2 assessments of included studies concerning risk of bias and concerns regarding applicability per study (a) and per QUADAS-2 domain (b) [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31].
See this image and copyright information in PMC

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