ReviewClinical oral investigations2022
Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges.
Review in Clinical oral investigations, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis.Head & face medicine · 2025Pooled it
- Artificial intelligence for facial attractiveness assessment in orthodontics: model development and evaluation.European journal of orthodontics · 2026Article
- LOESS-Based Cephalometric Growth Curves for Clinical Assessment of Craniofacial Development: A Cross-Sectional Study.Dentistry journal · 2026Article
- Navigating the ethical and legal landscape of artificial intelligence in dentistry.Journal of advanced periodontology & implant dentistry · 2026Article
- Artificial Intelligence in Australian Dental and General Healthcare: A Scoping Review.Australian dental journal · 2025Article
- Optimizing Ridge Augmentation With AI-Generated Models: A Case Report With Technical Note.Clinical case reports · 2025Article
- Comparative analysis of facial aesthetics in AI generated versus conventionally crafted digital smile designs-a cross-sectional study.BDJ open · 2025Article
- AI in Dentistry: Innovations, Ethical Considerations, and Integration Barriers.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Dentistry: A Descriptive Review.Bioengineering (Basel, Switzerland) · 2024Review
- Scoring facial attractiveness with deep convolutional neural networks: How training on standardized images reduces the bias of facial expressions.Orthodontics & craniofacial research · 2024Article
- Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review.Journal of personalized medicine · 2024Review
- Harnessing the Power of Artificial Intelligence in Cleft Lip and Palate: An In-Depth Analysis from Diagnosis to Treatment, a Comprehensive Review.Children (Basel, Switzerland) · 2024Review
- ChatGPT and its use in paediatric dentistry.European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry · 2023Article
- Artificial intelligence applications and ethical challenges in oral and maxillo-facial cosmetic surgery: a narrative review.Maxillofacial plastic and reconstructive surgery · 2023Review
- Defining medical liability when artificial intelligence is applied on diagnostic algorithms: a systematic review.Frontiers in medicine · 2023Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis review aims to share the current developments of artificial intelligence (AI) solutions in the field of medico-dental diagnostics of the face. The primary focus of this review is to present the applicability of artificial neural networks (ANN) to interpret medical images, together with the associated opportunities, obstacles, and ethico-legal concerns. MATERIAL AND
methodsNarrative literature review.
resultsNarrative literature review.
conclusionCurated facial images are widely available and easily accessible and are as such particularly suitable big data for ANN training. New AI solutions have the potential to change contemporary dentistry by optimizing existing processes and enriching dental care with the introduction of new tools for assessment or treatment planning. The analyses of health-related big data may also contribute to revolutionize personalized medicine through the detection of previously unknown associations. In regard to facial images, advances in medico-dental AI-based diagnostics include software solutions for the detection and classification of pathologies, for rating attractiveness and for the prediction of age or gender. In order for an ANN to be suitable for medical diagnostics of the face, the arising challenges regarding computation and management of the software are discussed, with special emphasis on the use of non-medical big data for ANN training. The legal and ethical ramifications of feeding patients' facial images to a neural network for diagnostic purposes are related to patient consent, data privacy, data security, liability, and intellectual property. Current ethico-legal regulation practices seem incapable of addressing all concerns and ensuring accountability. CLINICAL SIGNIFICANCE: While this review confirms the many benefits derived from AI solutions used for the diagnosis of medical images, it highlights the evident lack of regulatory oversight, the urgent need to establish licensing protocols, and the imperative to investigate the moral quality of new norms set with the implementation of AI applications in medico-dental diagnostics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.