Evidence map›Paper›PMID 36153437›Full record

ReviewClinical oral investigations2022

Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges.

Raphael Patcas, Michael M Bornstein, Marc A Schätzle, Radu Timofte

Open access · hybridAbstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
6.0field-weighted citation impact, top 3% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Navigating the ethical and legal landscape of artificial intelligence in dentistry.Journal of advanced periodontology & implant dentistry · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Artificial Intelligence in Dentistry: A Descriptive Review.Bioengineering (Basel, Switzerland) · 2024
    Review
  10. Article
  11. Review
  12. Review
  13. ChatGPT and its use in paediatric dentistry.European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry · 2023
    Article
  14. Review
  15. Review
  16. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Raphael PatcasClinic of Orthodontics and Pediatric Dentistry, Center of Dental Medicine, University of Zurich, Zurich, Switzerland. Raphael.patcas@zzm.uzh.ch.ORCID http://orcid.org/0000-0003-3906-9083
Michael M BornsteinDepartment of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland.
Marc A SchätzleClinic of Orthodontics and Pediatric Dentistry, Center of Dental Medicine, University of Zurich, Zurich, Switzerland.
Radu TimofteComputer Vision Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland.
University of Zurich · CHUniversity of Basel · CHUniversity of Würzburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Artificial IntelligenceHumansArtificial intelligenceFaceGovernment regulation and oversightNeural networksPhotography

Identifiers

PMID36153437
PMCPMC9708749
OpenAlexW4296949118

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.