This paper introduces DiffTumor, a novel framework for generalizable tumor synthesis, which enables the creation of realistic tumors in medical images. The key observation is that early-stage tumors (<2cm) exhibit similar imaging characteristics in computed tomography (CT) scans across different organs, such as the liver, pancreas, and kidneys. This framework leverages generative AI models, specifically Diffusion Models, to create synthetic tumors that can be generalized to various organs even with limited annotated data from a single organ. The process involves three stages: training an Autoencoder Model to learn latent features from unlabeled CT volumes, training a Diffusion Model using these latent features and tumor masks, and training a Segmentation Model using synthetic tumors and their masks. The results demonstrate that DiffTumor can generate visually realistic tumors that improve the generalizability of AI models for tumor detection and segmentation across different organs and patient demographics. The framework also reduces the need for extensive annotations and accelerates tumor synthesis, making it a valuable tool for enhancing the performance of AI models in medical imaging.This paper introduces DiffTumor, a novel framework for generalizable tumor synthesis, which enables the creation of realistic tumors in medical images. The key observation is that early-stage tumors (<2cm) exhibit similar imaging characteristics in computed tomography (CT) scans across different organs, such as the liver, pancreas, and kidneys. This framework leverages generative AI models, specifically Diffusion Models, to create synthetic tumors that can be generalized to various organs even with limited annotated data from a single organ. The process involves three stages: training an Autoencoder Model to learn latent features from unlabeled CT volumes, training a Diffusion Model using these latent features and tumor masks, and training a Segmentation Model using synthetic tumors and their masks. The results demonstrate that DiffTumor can generate visually realistic tumors that improve the generalizability of AI models for tumor detection and segmentation across different organs and patient demographics. The framework also reduces the need for extensive annotations and accelerates tumor synthesis, making it a valuable tool for enhancing the performance of AI models in medical imaging.