{
  "abstract": "Artificial intelligence is transforming dental wear assessment, but clinically useful systems should forecast pathological progression rather than physiological wear associated with normal function and ageing. Current research follows 2 largely separate paths: models for natural teeth detect or grade existing wear and automate comparisons of serial intraoral scans, whereas restorative-material models predict laboratory wear under controlled tribological conditions. No clinically validated system currently forecasts patient-specific future wear across natural teeth, restorations, and antagonists. This perspective proposes a multimodal longitudinal framework combining serial surface geometry with substrate identity, restoration design, occlusal function, patient behaviour, and biological exposures. It defines clinically meaningful prediction targets and presents a staged roadmap for data harmonisation, external validation, clinical impact evaluation, and responsible implementation. Such systems could improve personalised recall, prevention, material selection, and timely intervention, although laboratory accuracy should not be interpreted as clinical certainty.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Conservative Dentistry and Prosthodontics, Faculty of Dentistry, Complutense University of Madrid, Madrid, Spain; Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India; Department of Prosthodontics, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Seyed Ali Mosaddad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fj522stay0xg8.bib",
  "cited_by": 0,
  "doi": "10.1016/j.identj.2026.111250",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S0020653926018393",
  "id": "fj522stay0xg8",
  "issns": [
    "0020-6539",
    "1875-595X"
  ],
  "landing_url": "https://doi.org/10.1016/j.identj.2026.111250",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RK1-715"
    ],
    [
      "openalex",
      "3505|Dental Erosion and Treatment"
    ]
  ],
  "openalex": "W7214035027",
  "pdf_url": "https://www.sciencedirect.com/science/article/pii/S0020653926018393/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/fj522stay0xg8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:43:44Z",
      "native_id": "a338a977afa849b2ad31a2f9db779fa0",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.identj.2026.111250"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RK1-715",
      "subfield": "general"
    }
  ],
  "title": "Predicting Pathological Dental Wear: A Roadmap for Artificial Intelligence in Natural Teeth and Restorations",
  "title_text": "Predicting Pathological Dental Wear: A Roadmap for Artificial Intelligence in Natural Teeth and Restorations",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:44Z",
  "url": "https://research.draconitic.com/paper/fj522stay0xg8",
  "venue": "International Dental Journal",
  "versions": []
}