Applications of artificial intelligence in diagnostic and restorative planning

Abstract

Objective: To analyze the applications of artificial intelligence (AI) in diagnosis and restorative planning in dentistry, emphasizing clinical benefits, limitations, and implications for incorporating these technologies into professional practice. Methods: This integrative literature review was conducted using the PubMed/MEDLINE, Scopus, and SciELO databases, including studies published between 2015 and 2025 in Portuguese, English, or Spanish. Terms related to artificial intelligence, machine learning, deep learning, dentistry, dental diagnosis, restorative planning, and CAD/CAM were used. Full articles addressing AI applications in diagnosis, prognosis, and restorative planning in dentistry were included, with thematic synthesis of findings. Results: Twenty-two studies composed the final sample. There was a predominance of international studies focused on image analysis, prosthetic design, and automation of restorative workflows. The studies demonstrated promising performance of deep learning and convolutional neural network models in detecting carious lesions and AI applications for finish line detection, crown design, restoration recognition, and integration with CAD/CAM workflows. Final considerations: AI demonstrates potential as a complementary tool to clinical reasoning, improving diagnostic accuracy, operative standardization, and restorative predictability. However, routine incorporation still depends on external validation, algorithmic transparency, data protection, professional training, and integration into clinical workflows.