Evidence map›Paper›PMID 40011130›Full record

ReviewInternational dental journal2025

The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview Part 2: The Promise and Perils, and the International Dental Federation Communique.

Nozimjon Tuygunov, Lakshman Samaranayake, Zohaib Khurshid, Paak Rewthamrongsris, Falk Schwendicke, Thanaphum Osathanon, Noor Azlin Yahya

Abstract readReview
In one paragraph

Review in International dental journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
94citing papers in PubMed, 8 pooled it
–field-weighted citation impact
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

94 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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34 more citing papers are in PubMed but not listed here.

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

7 authors.

Nozimjon TuygunovFaculty of Dentistry, Kimyo International University in Tashkent, Tashkent, Uzbekistan. Electronic address: nozimtuygunov@gmail.com.
Lakshman SamaranayakeFaculty of Dentistry, University of Hong Kong, Sai Ying Pun, Hong Kong; Dr DY Patil Dental College and Hospital, Dr DY Patil Vidyapeeth, Pimpri, Pune, India; Center of Excellence for Dental Stem Cell Biology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand. Electronic address: lakshman@hku.hk.
Zohaib KhurshidDepartment of Prosthodontics and Dental Implantology, College of Dentistry, King Faisal University, Al-Ahsa, Saudi Arabia; Department of Anatomy, Faculty of Dentistry, Center of Excellence for Regenerative Dentistry, Chulalongkorn University, Bangkok, Thailand.
Paak RewthamrongsrisCenter of Excellence for Dental Stem Cell Biology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand.
Falk SchwendickeClinic for Conservative Dentistry and Periodontology, University Hospital of the Ludwig-Maximilians- University Munich, Munich, Germany.
Thanaphum OsathanonCenter of Excellence for Dental Stem Cell Biology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand.
Noor Azlin YahyaClinic for Conservative Dentistry and Periodontology, University Hospital of the Ludwig-Maximilians- University Munich, Munich, Germany; Department of Restorative Dentistry, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the final part of this two part article on artificial intelligence (AI) in dentistry we review its transformative role, focusing on AI in dental education, patient communications, challenges of integration, strategies to overcome barriers, ethical considerations, and finally, the recently released International Dental Federation (FDI) Communique (white paper) on AI in Dentistry. AI in dental education is highlighted for its potential in enhancing theoretical and practical dimensions, including patient telemonitoring and virtual training ecosystems. Challenges of AI integration in dentistry are outlined, such as data availability, bias, and human accountability. Strategies to overcome these challenges include promoting AI literacy, establishing regulations, and focusing on specific AI implementations. Ethical considerations in AI integration within dentistry, such as patient privacy and algorithm bias, are emphasized. The need for clear guidelines and ongoing evaluation of AI systems is crucial. The FDI White Paper on AI in Dentistry provides insights into the significance of AI in oral care, dental education, and research, along with standards for governance. It discusses AI's impact on individual patients, community health, dental education, and research. The paper addresses biases, limited generalizability, accessibility, and regulatory requirements for AI in dental practice. In conclusion, AI plays a significant role in modern dental care, offering benefits in diagnosis, treatment planning, and decision-making. While facing challenges, strategic initiatives focusing on AI literacy, regulations, and targeted implementations can help overcome barriers and maximize the potential of AI in dentistry. Ethical considerations and ongoing evaluation are essential for ensuring responsible, effective and efficacious deployment of AI technologies in dental ecosystem.

Indexed as

Artificial IntelligenceDentistryEducation, DentalHumansAI IntegrationArtificial Intelligence (AI)EthicsMachine LearningPatient Communication

Identifiers

PMID40011130
PMCPMC11976557

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.