Artificial Intelligence (AI) in Dental Clinical Practice
A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Dentistry, Oral Surgery and Oral Medicine".
Deadline for manuscript submissions: 20 January 2026
Special Issue Editors
Interests: biomechanics of temporomandibular joint; temporomandibular disorders; tissue engineering; therapeutic ultrasound; clinical orthodontics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Background: Artificial intelligence (AI) has become increasingly integrated into various aspects of society, including healthcare. In dentistry, the adoption of AI technologies is gradually gaining recognition for their potential to enhance diagnostic accuracy, streamline clinical workflows, and support personalized treatment plans based on large-scale data analysis.
Aim and scope: This Special Issue aims to provide a scientific forum for exploring and critically evaluating the application of AI in dental clinical practice. We particularly emphasize evidence-based validation and the need to ensure transparency, reliability, and accuracy in AI-driven systems used for clinical examination, diagnosis, and treatment planning.
History: Although AI has already shown significant progress in general medicine—such as radiology and pathology—its implementation in dentistry remains in an early but rapidly evolving stage. The recent emergence of machine learning, deep learning, and advanced image-processing techniques has led to a dramatic increase in the number of AI tools, many of which still require robust clinical evaluation.
We encourage submissions that address both technological innovation and clinical relevance, particularly studies on automated diagnostic algorithms, AI-driven radiographic interpretation, and real-time clinical decision support. Contributions analyzing the ethical, legal, and social implications of using AI in dental settings are also highly welcome.
We welcome high-quality original research articles, comprehensive reviews, and detailed case studies that rigorously examine AI technologies across all dental specialties. Submissions should highlight the limitations of AI decision-making, the integrity and transparency of training datasets, and the essential role of clinician oversight in AI-assisted care.
Prof. Dr. Eiji Tanaka
Dr. Keiichirou Wataneb
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence
- dental diagnostics
- machine learning
- deep learning
- digital dentistry
- clini-cal decision support
- diagnostic automation
- data transparency
- evidence-based dentistry
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