Applications of artificial intelligence in dentistry: an evidence and gap map analysis

Artificial Intelligence (AI) is rapidly emerging as a transformative technology in healthcare, including the field of dentistry, by facilitating intelligent data processing, diagnostic precision, and patient-centred engagement. The current synthesized evidence on AI applications within dentistry is fragmented, with limited clarity regarding the distribution across clinical domains and application areas. To systematically identify, categorize, and map the existing body of synthesized evidence on AI applications in dentistry using the Evidence and Gap Map (EGM) framework, thereby delineating areas of research concentration and highlighting critical evidence gaps. A systematic search across multiple electronic databases yielded 523 studies, of which 426 remained after deduplication. From these, 155 systematic reviews and meta-analyses fulfilled the inclusion criteria and were incorporated into the final EGM. Each study was coded into a matrix based on dental condition (e.g., caries, periodontitis, oral cancer) and AI application area (e.g., diagnosis, prognosis, treatment planning, imaging). Data visualization was performed using EPPI-Mapper to assess the distribution of evidence and identify research gaps. A total of 155 systematic reviews and meta-analyses were included in the EGM, mapping diseases (rows) against outcomes (columns). The synthesized evidence is dominated by AI applications in diagnosis and detection—especially for oral cancer, PMDs, cysts, tumours, and soft tissue lesions—using ML and DL for radiographic and clinical analysis. Similar trends are seen in restorative and periodontal domains, while applications in treatment planning, rehabilitation, and therapy remain sparse. The EGM highlighted significant evidence synthesis gaps in several key areas, particularly prognosis and prediction of impaction and trauma, as well as imaging/radiology interpretation. This study used the Evidence and Gap Map (EGM) methodology to systematically assess and visualize current synthesized evidence on AI applications in dentistry. The resulting EGM offers a strategic framework to guide future research and support evidence-based AI integration in dental care.