<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2d1 20170631//EN" "JATS-journalpublishing1.dtd">
<article xlink="http://www.w3.org/1999/xlink" dtd-version="1.0" article-type="dentistry" lang="en">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher">JOHS</journal-id>
      <journal-id journal-id-type="nlm-ta">Journ of Health Scien</journal-id>
      <journal-title-group>
        <journal-title>Journal of HealthCare Sciences</journal-title>
        <abbrev-journal-title abbrev-type="pubmed">Journ of Health Scien</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">2231-2196</issn>
      <issn pub-type="opub">0975-5241</issn>
      <publisher>
        <publisher-name>Radiance Research Academy</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">332</article-id>
      <article-id pub-id-type="doi">http://dx.doi.org/10.52533/JOHS.2024.41211</article-id>
      <article-id pub-id-type="doi-url"/>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Dentistry</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>The Impact of AI and Machine Learning in Predicting the Success of Dental Restorations&#13;
</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Almossaen</surname>
            <given-names>Mohammad Bakheet</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Eskandrani</surname>
            <given-names>Rayan Mohmoud</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Albalawi</surname>
            <given-names>Badr Falah</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Aldahian</surname>
            <given-names>Nada Abdullah</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>ALToukhi</surname>
            <given-names>Hayat Hussain</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Aldakkan</surname>
            <given-names>Bayan Ahmad</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date pub-type="ppub">
        <day>8</day>
        <month>12</month>
        <year>2024</year>
      </pub-date>
      <volume>4</volume>
      <issue>12</issue>
      <fpage>713</fpage>
      <lpage>720</lpage>
      <permissions>
        <copyright-statement>This article is copyright of Popeye Publishing, 2009</copyright-statement>
        <copyright-year>2009</copyright-year>
        <license license-type="open-access" href="http://creativecommons.org/licenses/by/4.0/">
          <license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence, and indicate if changes were made.</license-p>
        </license>
      </permissions>
      <abstract>
        <p>Artificial intelligence (AI) and machine learning (ML) are revolutionizing the field of dentistry by offering advanced tools for predicting the success of dental restorations. These technologies enable the analysis of large datasets to identify patterns and correlations that inform clinical decision-making. By leveraging algorithms such as convolutional neural networks and decision trees, AI systems can assess patient-specific factors, procedural variables, and material properties with unprecedented accuracy. Applications include analyzing stress and strain patterns in restorative materials, detecting microleakages and marginal discrepancies through imaging data, and predicting long-term outcomes based on patient follow-ups. Machine learning models, particularly deep learning architectures, excel in processing complex datasets, such as 3D imaging and intraoral scans, enabling personalized treatment plans and optimizing restoration design. AI-based diagnostic tools have integrated into clinical workflows to enhance procedural precision, offering real-time feedback during treatments and assisting with material selection tailored to individual needs. These advancements have not only improved patient outcomes but have also streamlined clinical workflows by reducing human error and variability. However, challenges such as data fragmentation, algorithm interpretability, and ethical considerations remain significant barriers to widespread adoption. Efforts to standardize datasets, develop explainable AI models, and address data privacy concerns are critical for overcoming these limitations. Future developments in AI-powered dental applications include interdisciplinary collaborations, integration with preventive dentistry, and the expansion of personalized care. These innovations hold the potential to reshape restorative dentistry, offering enhanced diagnostic accuracy, efficient workflows, and improved patient satisfaction.&#13;
</p>
      </abstract>
      <kwd-group>
        <kwd> artificial intelligence</kwd>
        <kwd> machine learning</kwd>
        <kwd> dental restorations</kwd>
        <kwd> predictive modeling</kwd>
        <kwd> restorative dentistry</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>