Full paper: Büttner, M., Rokhshad, R., Brinz, J., Issa, J., Chaurasia, A., Uribe, S.E., Karteva, T., Chala, S., Tichy, A., Schwendicke, F., 2024. Core outcomes measures in dental computer vision studies (DentalCOMS). J. Dent. 150, 105318. https://doi.org/10.1016/j.jdent.2024.105318

This scoping review examines existing university and organisational guidelines on integrating generative AI (GenAI), such as ChatGPT, into education, focusing on recommendations for academic staff. The authors analysed 31 documents from various institutions and organisations globally, identifying common themes like ethical use, transparency, and academic integrity. A significant lack of dental-specific guidance was highlighted, prompting recommendations for creating comprehensive guidelines that address ethical concerns, prevent misuse, and promote critical thinking. The review also discusses the need for AI literacy training and infrastructure investment to facilitate effective GenAI integration in dental education. Finally, the authors suggest that dental institutions should adapt with the same urgency shown during the COVID-19 pandemic to successfully integrate this technology.

Paper: Uribe, S.E., Maldupa, I., Schwendicke, F., 2025. Integrating Generative AI in Dental Education: A Scoping Review of Current Practices and Recommendations. Eur. J. Dent. Educ. https://doi.org/10.1111/eje.13074

In this episode, we explore the state of transparency in dental research, based on a large-scale analysis of the dental literature. The study examined over 10,000 articles from PubMed-indexed dental journals, assessing key transparency practices such as data sharing, code sharing, and conflict of interest disclosure. While some practices, like funding disclosures, are improving, data and code sharing remain rare. Join us as we discuss what these findings mean for the future of research integrity and why dental science needs more openness to drive progress.

Full text: Research transparency in dental research: A programmatic analysis