Generative artificial intelligence (GAI) is increasingly used to create synthetic datasets (SDs) in dentistry to overcome challenges in traditional data acquisition. AI, particularly deep learning (DL), requires large, diverse datasets for optimal performance. However, traditional datasets face issues such as privacy concerns, data annotation challenges, and biases due to underrepresentation of certain populations. These issues hinder the generalizability and applicability of AI models in healthcare. SDs generated using GAI techniques like variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models can address these challenges. VAEs use probabilistic methods to generate diverse data, while GANs involve a competition between a generator and a discriminator to produce realistic data. Diffusion models generate data through a process of adding and removing noise. SDs offer benefits in research, education, and clinical applications. They can enhance AI model robustness, support personalized training for medical students, and improve data availability for rare conditions. However, SDs also pose challenges, including potential re-identification of patients, biases from human involvement in data creation, and the need for evaluation metrics to ensure synthetic data is realistic and unbiased. The use of SDs in healthcare requires careful consideration of ethical and technical issues to ensure fairness and accuracy. AI governance is essential to ensure that GAI is implemented in a way that benefits society. Future research should focus on improving the reliability and fairness of SDs, as well as addressing biases in AI development.Generative artificial intelligence (GAI) is increasingly used to create synthetic datasets (SDs) in dentistry to overcome challenges in traditional data acquisition. AI, particularly deep learning (DL), requires large, diverse datasets for optimal performance. However, traditional datasets face issues such as privacy concerns, data annotation challenges, and biases due to underrepresentation of certain populations. These issues hinder the generalizability and applicability of AI models in healthcare. SDs generated using GAI techniques like variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models can address these challenges. VAEs use probabilistic methods to generate diverse data, while GANs involve a competition between a generator and a discriminator to produce realistic data. Diffusion models generate data through a process of adding and removing noise. SDs offer benefits in research, education, and clinical applications. They can enhance AI model robustness, support personalized training for medical students, and improve data availability for rare conditions. However, SDs also pose challenges, including potential re-identification of patients, biases from human involvement in data creation, and the need for evaluation metrics to ensure synthetic data is realistic and unbiased. The use of SDs in healthcare requires careful consideration of ethical and technical issues to ensure fairness and accuracy. AI governance is essential to ensure that GAI is implemented in a way that benefits society. Future research should focus on improving the reliability and fairness of SDs, as well as addressing biases in AI development.