With the increasing effectiveness of medical imaging in clinical diagnosis, how to efficiently and accurately identify complex pathological patterns in images has become a key technical problem. In particular, the growth of deep learning and graph neural networks has provided new ideas for addressing the problems of multi-scale feature extraction, heterogeneous data fusion and interpretability in image recognition. This paper focuses on the core challenges of complex pattern recognition and systematically analyzes the capabilities and limitations of current AI models in feature modeling, decision reasoning and clinical integration. It focuses on exploring the application effects of multi-scale residual structure, joint learning and transfer learning in enhancing recognition performance. With the help of the combination of the US public medical imaging database and clinical trial data, the positive role of model optimization in promoting actual diagnostic accuracy and efficiency has been verified. The paper further proposes a solution for creating an intelligent diagnosis and treatment system based on visualization mechanism and human-computer collaborative design, with the goal of improving the usability and reliability of AI systems in clinical environments. The future development of medical AI should focus on system-level fusion, multimodal reasoning and clinical semantic alignment, promote the transformation of artificial intelligence from technological breakthroughs to clinical implementation in the field of medical image diagnosis, and play a key supporting role in precision medicine.

Chun Chen. Complex Pattern Recognition and Clinical Application of Artificial Intelligence in Medical Imaging Diagnosis. International Journal of Health and Pharmaceutical Medicine (2025), Vol. 5, Issue 1: 1-10. https://doi.org/10.38007/IJHPM.2025.050101.

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