Current evidence of generative artificial intelligence specifically developed for dental and maxillofacial radiology: a systematic review.

OBJECTIVES: This systematic review aimed to investigate the current development and application landscape of generative artificial intelligence (Gen-AI) networks specifically designed for dental and maxillofacial radiology (DMFR) and summarize their potential applications for clinical practice, education, and research. METHODS: Five electronic databases were searched to identify studies that developed and validated DMFR-specific Gen-AI networks. Data regarding the purpose and type of the Gen-AI model, dataset details, quantitative evaluation metrics, methods of subjective assessment, and key findings were extracted. Customized assessment criteria adapted from the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) checklist were used to evaluate the risk-of-bias for the included studies in 4 domains (dataset details, reporting of Gen-AI model architecture and training strategies, reliability of performance evaluation methods, and accessibility of code and developed models). RESULTS: Forty-three studies were included from the initially identified 2060 records. Of these, 24 studies (55.8%) focused on image quality, addressing improvements in spatial resolution, artifact and noise reduction, image geometry and projection, quantitative accuracy of voxel values, 14 (32.6%) on image simulation (including the generation of bitewing, panoramic, and cephalometric radiographs, image-to-image translation, and post-treatment prediction simulation), 3 (7%) on 3D reconstruction from 2D images, and 2 (4.6%) on automated interpretation and reporting. Nearly all included studies (95.3%) reported objective evaluation metrics while about half (58.1%) incorporated subjective assessments using scoring systems or visual grading. Risk-of-bias was moderate for dataset details in 7 studies (16.3%) and performance evaluation in 20 (46.5%), and high for code and model accessibility in 35 studies (81.4%). CONCLUSIONS: While DMFR-specific Gen-AI models show promising potential for clinical practice, education, and research, their applicability requires overcoming challenges related to data quality, validation, integration, and ethical and legal considerations. Further clinical validation, increased transparency and accessibility, and thorough evaluation of cost-effectiveness in diagnostic workflows are essential to ensure their safe and effective use.