An auxiliary diagnostic model based on joint learning of brain and lung data

Artificial intelligence has significantly improved diagnostic accuracy and efficiency in medical imaging-assisted diagnosis. However, existing systems often focus on a single disease, neglecting the pathological connections between diseases. To fully leverage multi-disease information, this paper proposes an auxiliary diagnostic model based on joint learning of brain and lung data (ADMBLD), aiming to enhance the comprehensiveness and accuracy of diagnoses through cross-disease correlation learning. The model integrates imaging data and clinical history of brain and lung diseases to identify potential correlations between different diseases. Experimental results show that the model trained on both brain and lung data outperforms those trained separately, validating the effectiveness of the multi-disease joint learning diagnostic model. This confirms that integrating multi-disease information captures latent pathological relationships, overcoming the limitations of single-disease models, thereby providing clinicians with more precise and comprehensive diagnostic support and demonstrating its potential in advancing intelligent diagnostic systems.