Utilization of artificial intelligence-based pulmonary nodule target reconstruction software in clinical practice education for standardized training residents.

BACKGROUND

The precise evaluation of pulmonary nodules via computed tomography (CT) is pivotal in clinical decision-making and patient prognosis. Artificial intelligence (AI)-assisted target reconstruction technology for pulmonary nodules can more clearly display the target nodule and its relationship with surrounding structures, aiding radiologists in higher diagnostic accuracy. Nevertheless, few studies have examined the impact of such AI-based software on the education and training of residents. This study aims to investigate the role of AI-assisted pulmonary nodule target reconstruction software in enhancing the diagnostic capabilities of residents from different specialties and to explore differences between three different learning modes, thereby preliminarily assessing the significance of AI technology in clinical training of medical imaging for residents.

METHODS

Seventy-five standardized training residents from various specialties, including 32 radiology and 43 non-radiology residents, participated in rotations in our radiology department between August 2020 and September 2023. Following a four-week period of training and learning with AI-assisted pulmonary nodule target reconstruction software and the traditional picture archiving and communication system (PACS), the diagnostic capabilities of both radiology and non-radiology residents in evaluating pulmonary nodule cases were assessed. Additionally, the differences in their ability to assess and diagnose pulmonary nodules under three distinct learning modes assisted by AI software (full-application, cross-application, and interval-application) were analyzed.

RESULTS

After four weeks of training, the diagnostic accuracy of radiology residents for five test pulmonary nodule cases ranged from 96.88 to 100%, outperforming non-radiology residents, whose accuracy ranged from 67.44 to 86.04%. Among the 54 residents trained under three predefined learning modes, significant differences were found in pulmonary nodule assessment scores. Pairwise comparisons using the Tukey-Kramer test revealed that the full-application group scored lower compared to both the cross-application ( = 0.002) and interval-application ( = 0.004) groups, with the latter two demonstrating superior performance.

CONCLUSION

AI-assisted target reconstruction and assessment software for pulmonary nodules is found to be valuable in medical imaging education and training. A hybrid learning approach that integrates AI software with traditional PACS may be more effective in enhancing the pulmonary nodule assessment and diagnostic capabilities of residents.