The article discusses the challenges and potential solutions in generating synthetic datasets (SDs) for artificial intelligence (AI) in dentistry. Traditional datasets face issues such as privacy concerns, manual annotation, and biases, which hinder their generalizability and applicability across different populations. Generative AI techniques, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models, are proposed to overcome these challenges by producing diverse and realistic synthetic data. VAEs use probabilistic techniques to generate new data points, GANs involve a generator and a discriminator to create realistic images, and diffusion models add and remove noise to generate new images. These methods can enhance AI models' performance and applicability, but they also present challenges such as patient re-identification, lack of evaluation metrics, and the risk of generating false or hallucinated data. The authors emphasize the need for better understanding and addressing these limitations before widespread adoption of synthetic data in healthcare.The article discusses the challenges and potential solutions in generating synthetic datasets (SDs) for artificial intelligence (AI) in dentistry. Traditional datasets face issues such as privacy concerns, manual annotation, and biases, which hinder their generalizability and applicability across different populations. Generative AI techniques, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models, are proposed to overcome these challenges by producing diverse and realistic synthetic data. VAEs use probabilistic techniques to generate new data points, GANs involve a generator and a discriminator to create realistic images, and diffusion models add and remove noise to generate new images. These methods can enhance AI models' performance and applicability, but they also present challenges such as patient re-identification, lack of evaluation metrics, and the risk of generating false or hallucinated data. The authors emphasize the need for better understanding and addressing these limitations before widespread adoption of synthetic data in healthcare.