{
  "abstract": "Artificial intelligence is increasingly being applied to endoscopic ultrasonography for gastrointestinal subepithelial lesions, but the maturity of the evidence differs substantially across clinical tasks. This narrative review critically appraises contemporary studies of lesion detection and localization, wall-layer identification, diagnostic classification, pathological risk classification of gastrointestinal stromal tumors, human–artificial intelligence interaction, and potential management support. Thirty-one original studies published from 2021 through 2026 were included in the contemporary evidence map. Binary differentiation of gastrointestinal stromal tumors from leiomyomas or other selected comparators has the largest evidence base and includes multicenter, external, prospective, and real-time evaluations. Evidence for multiclass diagnosis, layer-of-origin assessment, small lesions, and pathological risk classification is less mature and is strongly influenced by case spectrum, reference standard, equipment, image selection, and validation design. No study has demonstrated that artificial intelligence improves tissue-acquisition strategy, surveillance, treatment selection, or patient outcomes in subepithelial lesions. Progress toward clinically useful decision support will require patient-level data separation, device- and center-held-out validation, analysis of complete examinations rather than curated images, explicit uncertainty handling, prospective human–artificial intelligence studies, and trials with endoscopy-specific management and safety outcomes.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy",
    "IHU-Strasbourg, Institute of Image-Guided Surgery, Université de Strasbourg, 67000 Strasbourg, France",
    "Gastroenterology and Digestive Endoscopy Unit, Azienda Sanitaria Universitaria Integrata del Trentino (ASUIT), 38122 Trento, Italy",
    "Unit of Gastroenterology, Ospedale Maggiore Della Carità di Novara, 28100 Novara, Italy",
    "Gastroenterology and Endoscopy Department, Koelliker Hospital, 10134 Torino, Italy",
    "Department of Gastroenterology and Hepatology, CHU UCL Namur, Université Catholique de Louvain, 5530 Yvoir, Belgium",
    "Institute for Translational Medicine and Liver Disease, ITM, UMRS_1110, Université de Strasbourg, 67000 Strasbourg, France",
    "Department of Gastroenterology and Hepatology, Hôpitaux Universitaires de Strasbourg, Université de Strasbourg, 67000 Strasbourg, France"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Elettra Merola",
      "orcid": "0000-0001-9553-7684"
    },
    {
      "affs": [
        1
      ],
      "name": "Leonardo Sosa-Valencia",
      "orcid": "0000-0001-6594-4748"
    },
    {
      "affs": [
        2
      ],
      "name": "Andrea Michielan"
    },
    {
      "affs": [
        3
      ],
      "name": "Nico Pagano"
    },
    {
      "affs": [
        0
      ],
      "name": "Maria Pina Dore",
      "orcid": "0000-0001-7305-3531"
    },
    {
      "affs": [
        4
      ],
      "name": "Claudio De Angelis"
    },
    {
      "affs": [
        1,
        5,
        6,
        7
      ],
      "name": "Abdenor Badaoui",
      "orcid": "0009-0002-1202-7967"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7k96f1cvrayks.bib",
  "cited_by": 0,
  "doi": "10.3390/jcm15218440",
  "fulltext_url": null,
  "id": "7k96f1cvrayks",
  "issns": [
    "2077-0383"
  ],
  "landing_url": "https://www.mdpi.com/2077-0383/15/21/8440",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/7k96f1cvrayks.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:21:07Z",
      "native_id": "10.3390/jcm15218440",
      "source": "crossref",
      "url": "https://www.mdpi.com/2077-0383/15/21/8440"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "title": "Artificial Intelligence in Endoscopic Ultrasonography for Gastrointestinal Subepithelial Lesions: Evidence, Validation, and Clinical Translation",
  "title_text": "Artificial Intelligence in Endoscopic Ultrasonography for Gastrointestinal Subepithelial Lesions: Evidence, Validation, and Clinical Translation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:21:04Z",
  "url": "https://research.draconitic.com/paper/7k96f1cvrayks",
  "venue": "Journal of Clinical Medicine",
  "versions": []
}