Application of Conditional Generative AI in Tumor Diagnosis: A Systematic Review from Theory to Clinical Practice

This article systematically reviews the latest progress of conditional generative AI (GANs, VAEs, diffusion models, etc.) in tumor diagnosis. Key points include: Cancer is the second leading cause of death globally, and early accurate diagnosis is crucial for improving survival rates, but traditional diagnosis faces challenges such as complex pathological images and difficulty in multimodal integration; conditional generative AI optimizes the diagnostic process through data augmentation, cross-modal fusion, missing data completion, etc.; it also discusses technical challenges and clinical integration paths, providing directions for the development of precision oncology.

Cancer is the second leading cause of death globally, claiming nearly 10 million lives each year. Early accurate diagnosis is key to improving patient survival rates. However, traditional tumor diagnosis faces many challenges: high complexity of pathological images, difficulty in integrating multimodal data, scarcity of rare case data, and subjective differences in the diagnostic process. The development of artificial intelligence, especially deep learning technology, brings new hope for solving these these problems, among which conditional generative AIAI shows unique value.

Generative Adversarial Networks (GANs)

Comprehensively analyze the latest progress of conditional generative AI technologies (GANs, VAEs, diffusion models, etc.) in tumor diagnosis, and discuss key challenges and solutions such as multimodal fusion, missing data completion, and human-machine collaboration.