With the rapid advancement of artificial intelligence (AI), its applications now include screening, imaging, pathology, and multi-omics analysis. By processing large volumes of data, such as medical images, electronic health records, and genomic or transcriptomic features, AI can improve diagnostic accuracy and reduce costs. Dentistry and ophthalmology face unique challenges: complex anatomy, subtle early lesions, cumbersome grading processes, and systemic diseases that affect both oral and visual health. Therefore, comprehensive AI solutions are urgently needed to provide end-to-end intelligence from image recognition to gene transcription prediction.

Dental conditions such as periodontitis, caries, and oral cancer still depend on visual examination, radiographic imaging (panoramic X-rays, CBCT), and manual pathology grading. These methods are time-consuming and prone to inter-rater variability. Although extensive genomic and transcriptomic data exist, few studies have integrated multi-omics profiles into clinical diagnostic models, and methods to translate transcriptomic signatures into actionable predictions remain underdeveloped. Similarly, ophthalmic diseases including cataracts, glaucoma, and diabetic retinopathy require very high diagnostic precision because early signs can be subtle and image interpretation depends heavily on expert judgment; models that use transcriptomic information to predict disease progression or treatment response are still lacking. AI approaches that combine deep learning, statistical techniques, and multi-modal fusion can bridge these gaps by extracting key features from imaging, pathology, electronic health records, and genomic or transcriptomic data. They enable faster screening, more consistent grading, and more reliable gene transcription-based predictions.

This special issue aims to explore AI-driven, end-to-end solutions for dental and ophthalmic conditions, covering every stage from early screening and imaging analysis to pathological grading and gene transcription prediction.  Submissions (research articles or reviews) related to, but not limited to, the following topics are welcome:

• Multi-scale neural networks for dental and ocular lesion segmentation
• Interpretable AI for oral and ocular pathology slide grading
• Multi-omics integration for biomarker discovery in dental and ocular diseases
• Large language models for cross-modal medical text and image diagnosis
• AI clinical decision support systems for dentistry and ophthalmology
• Single-cell and spatial transcriptomics in oral and ocular research
• Molecular subtyping and prognosis models for oral and ocular cancers
• AI platforms for personalized dental and ocular treatment follow-up
• Intelligent computational methods for lesion parameters in oral and ocular diseases
• AI-driven assisted treatment technologies for oral and ocular diseases