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

• Published in: Journal of clinical medicine Vol. 12; no. 10; p. 3536
• Main Authors: Ewals, Lotte J. S., van der Wulp, Kasper, van den Borne, Ben E. E. M., Pluyter, Jon R., Jacobs, Igor, Mavroeidis, Dimitrios, van der Sommen, Fons, Nederend, Joost
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.05.2023; MDPI
• Subjects: Algorithms; Artificial intelligence; Bias; Business metrics; Clinical medicine; Computer-aided medical diagnosis; CT imaging; Diagnosis; Lung cancer; Lung diseases; Lung tumors; Medical imaging; Medical prognosis; Medical screening; Methods; Mortality; Patients; Practice; Quality standards; Radiologists; Systematic Review; Technology application; Netherlands; detection; lung neoplasms; tomography; diagnosis; malignancy prediction; systematic review; X-ray computed; artificial intelligence; computer-assisted
• ISSN: 2077-0383; 2077-0383
• DOI: 10.3390/jcm12103536

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.