<p>Research on the application of deep learning in the field of medical imaging has achieved significant progress, demonstrating exceptional performance, particularly in disease diagnosis and classification. Compared to clinicians, deep learning models can provide diagnostic results more rapidly for common diseases while maintaining stable accuracy, which holds positive implications for the smooth execution of clinical work. However, numerous challenges remain for the widespread application of deep learning models in clinical practice. Current research primarily focuses on the development of models, with insufficient attention paid to implementation issues in real-world applications. To address this research gap, this paper, based on the “input-model-output" framework of deep learning, delves into the challenges and corresponding solutions that models may encounter in practical applications from three dimensions. At the input level, we analyze data volume and feature selection, the complexity of model preprocessing steps, and the challenges of data fusion; at the model level, we primarily explore issues such as model generalizability, robustness, and interpretability; at the output level, we summarize three key modules: disease segmentation, diagnosis, and classification. Furthermore, this paper suggests potential future development directions, including the integration of interdisciplinary expert knowledge, the construction of novel medical models, the development of models for non-solid tumors, and the establishment of integrated multi-data web platforms. The research in this paper not only fills the final gap in the promotion and application of deep learning models in the medical field, making a positive contribution to their successful integration into clinical practice, but also provides clinical staff with a deeper perspective for understanding deep learning models, thereby facilitating the integration and collaborative development of the medical and artificial intelligence fields.</p>