Artificial Intelligence-Based Training for Pulmonary Nodule Diagnosis
Published in BMC Medical Education, July 2024
Background: The study aimed to assess the effectiveness of an artificial intelligence (AI)-assisted diagnosis system in training junior radiology residents and medical imaging students in detecting and diagnosing pulmonary nodules.
Methods: Participants were divided into three groups: two groups of medical imaging students and one group of junior radiology residents. They underwent training using traditional case-based teaching or the ‘AI intelligent assisted diagnosis system.’ After training, they performed localization, grading, and qualitative diagnosis of 1,057 lung nodules in 420 cases for seven rounds of testing. The study compared the sensitivity and false positive nodules in different densities, sizes, and positions among the groups.
Results: The study found that the AI-assisted training mode led to significant improvements in the detection rate, diagnostic compliance rate, and kappa scores. After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in the AI-assisted group. The average kappa score also showed a notable increase.
Conclusion: The AI-assisted diagnosis system proved to be a valuable tool for training junior radiology residents and medical imaging students in pulmonary nodule detection and diagnosis.
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