Data sharing for responsible artificial intelligence in dentistry: a narrative review of legal frameworks and...

Data sharing is essential for ensuring research reproducibility and for developing generalizable artificial intelligence (AI) systems, but it demands robust safeguards for patient privacy. This narrative review aims to guide dental clinicians and researchers in sharing patient data responsibly while preserving confidentiality.

Dental patient data include radiographs, (cone beam) CTs, photographs, intraoral scans, tabular data, and electronic health records. These datasets are often heterogeneous, distributed across institutions, and subject to strict privacy regulations. Handling and sharing such sensitive data requires secure, privacy-preserving techniques to ensure compliance with legal and ethical standards.

PubMed, Embase, Scopus, arXiv and Google Scholar were searched using keywords related to dentistry, data sharing, AI, and privacy-preserving techniques. Given the limited number of results relevant to dentistry, the search was extended to medicine. In parallel, we reviewed applicable regulatory frameworks such as the European Union (EU) General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), EU AI Act, and European Health Data Space (EHDS).

We selected studies addressing data sharing in dentistry/medicine, de-identification, privacy-preserving techniques, and/or federated learning, as well as applicable regulatory frameworks. Most of the articles were peer-reviewed, but authoritative grey literature was included as well.

This review summarized legal and technical aspects of dental data sharing to enable compliant multi-institutional collaboration. Beyond AI in dentistry, which was primarily emphasized, responsible data sharing is integral to FAIR practice and strengthens transparency and reproducibility across dental and medical research.

This review provides regulation-aligned guidance on de-identifying and sharing dental data, enabling compliant multi-institutional collaboration while protecting privacy. By promoting responsible AI development and reproducible research, it translates into more reliable care and greater patient trust in everyday clinical practice.