CHSJ

Curr Health Sci J, vol. 52, no. 1, 2026

Impact of AI Assistance on Radiologist Accuracy for Lung Nodule Detection on Chest CT

[Original Paper]

C.M. CIOFIAC(1,2), M.A. ENE(2,3), M.L. FLORESCU(1,3), R.V. TEICA(4), I.A. GHEONEA(1,3)


(1)Department of Radiology and Medical Imaging, University of Medicine and Pharmacy of Craiova, Romania;
(2)Doctoral School, University of Medicine and Pharmacy of Craiova, Romania;
(3)Craiova County Emergency Clinical Hospital, Romania;
(4)Department of Radiology, Faculty of Nursing, University of Medicine and Pharmacy of Craiova, Romania


Abstract:

Background: Accurate identification of pulmonary nodules as early-stage lung cancers is crucial to de-crease the number of deaths and illnesses caused by lung cancer. Artificial Intelligence has the potential to enhance diagnostic accuracy and specificity in detecting lung cancer. Methods: Chest CT scanning from 300 patients with an age range between 40 and 80 years old were analysed comparing the pulmonary nodules detection rate (number of lung nodules) between AI-assisted reading, non-AI-assisted reading and the AI-system report standalone. Detected nodules, missed nodules (false negatives), and false-positive findings were analysed. Results: AI-assisted radiologists missed significantly fewer nodules (p<0.001) and achieved an almost perfect correlation (r?1.00) with expert reference values, reducing the mean absolute error (MAE) from 9.78 to 0.46. Additionally, AI increased detection sensitivity from 60% to 98% and reduced false negatives from 3,083 to 145, optimizing both diagnostic accuracy and efficiency. Conclusions: AI-assisted reading has shown to be beneficial in the detection of lung nodules compared to relying solely on radiologist observation. This suggests that an AI-powered system for evaluating lung nodules has the potential to become a valuable assistant tool in clinical practice. By combining the skills of radiologists with AI assistance, a new approach may emerge, leading to enhanced detection of lung nodules and encouraging the integration of AI in lung cancer screening initiatives.


Keywords: Lung nodule, AI-assisted reading, chest computed tomography, lung cancer, artificial intelligence.



Corresponding: Rossy Vladu Teica, Department of Radiology, Faculty of Nursing, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania, e-mail: rossy.teica@gmail.com


DOI 10.12865/CHSJ.52.01.04 - Download PDF
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