{
  "abstract": "Uveitis encompasses a heterogeneous spectrum of etiologies, including systemic inflammatory diseases, infections, specific ophthalmologic entities, and pseudo-uveitis. The diagnostic approach relies on an algorithmic strategy aimed at the early identification of a curable etiology and/or an underlying condition suitable for targeted therapy, while taking into account the severity and relative frequency of the various causes. The prospective ULISSE study showed that a standardized diagnostic approach based on the anatomo-clinical classification of uveitis achieved a diagnostic yield comparable to unrestricted testing, while substantially reducing both the number and cost of investigations. Etiological diagnosis is primarily based on anatomo-clinical analysis of uveitis, integrating the site of inflammation, disease course, laterality, and specific ophthalmologic features. Subsequently, consideration of epidemiological and demographic factors, medical history, and extra-ophthalmologic clinical examination allows further refinement of the diagnostic workup. Complementary investigations should be prioritized and targeted, except for a minimal systematic baseline evaluation, as the diagnostic yield of non-directed testing is very low. Clinical decision-support tools based on artificial intelligence (AI) represent a major advancement. Machine learning models integrating clinical, ophthalmologic, laboratory, and imaging data available from the initial consultation achieve etiological prediction performances comparable to those of experts with access to a complete diagnostic workup. These multimodal AI models could transform diagnostic strategies by enabling a personalized etiological assessment based on the prioritization and dynamic adaptation of complementary investigations. However, their implementation in routine clinical practice will require prior medico-economic validation through controlled comparative studies.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Robin Jacquot"
    },
    {
      "name": "Thomas El-Jammal"
    },
    {
      "name": "Yvan Jamilloux"
    },
    {
      "name": "François Frapart"
    },
    {
      "name": "Thibaud Mathis"
    },
    {
      "name": "Laurent Kodjikian"
    },
    {
      "name": "Pascal Seve"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2h8z5y71p6jxz.bib",
  "cited_by": 0,
  "doi": "10.1016/j.revmed.2026.07.005",
  "fulltext_url": null,
  "id": "2h8z5y71p6jxz",
  "issns": [
    "0248-8663",
    "1768-3122"
  ],
  "landing_url": "https://hal.science/hal-05750618v1",
  "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": [
    [
      "hal",
      "0.sdv"
    ],
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      "hal",
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    ],
    [
      "openalex",
      "2731|Ocular Diseases and Behçet’s Syndrome"
    ]
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  "openalex": "W7202287117",
  "pdf_url": "https://www.sciencedirect.com/science/article/pii/S024886632600665X/pdf",
  "pmcid": null,
  "pmid": "42586902",
  "published": "2026-09-15",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/2h8z5y71p6jxz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:34Z",
      "native_id": "hal-05750618",
      "source": "hal",
      "url": "https://hal.science/hal-05750618v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:0.sdv",
      "subfield": "general"
    },
    {
      "field": "medicine",
      "origin": "hal:1.sdv.spee",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Diagnostic des uvéites : présent et futur",
  "title_text": "Diagnostic des uvéites : présent et futur",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:34Z",
  "url": "https://research.draconitic.com/paper/2h8z5y71p6jxz",
  "venue": "La Revue de Médecine Interne",
  "versions": []
}