{
  "abstract": "Artificial intelligence (AI) decision support systems for surgery hold promise but face barriers to adoption, particularly around the interpretability of their outputs. We conducted an international cross-sectional survey of 47 neurosurgeons to evaluate perspectives on literature-derived explanatory techniques for AI-generated anatomical segmentations, using endoscopic pituitary surgery as a high-risk exemplar. Participants ranked certainty scores, certainty maps, saliency maps, scene similarity scores, and nearest-neighbour illustrations, and rated them using a modified Explanation Satisfaction Scale alongside free-text feedback. Certainty-based techniques were consistently ranked and rated highest - valued for aligning with surgical decision-making by conveying confidence (via scores) and anatomical boundaries (via maps). Saliency- and similarity-based methods were judged less clinically relevant and better suited to educational settings. Therefore, certainty-based techniques appear most acceptable to surgeons for improving the interpretability of AI-assisted anatomical navigation systems, though their impact on trust calibration and performance requires prospective evaluation across surgical domains.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 58,
  "authors": [
    {
      "name": "Danyal Zaman Khan"
    },
    {
      "name": "Tobias Joseph Adams"
    },
    {
      "name": "Anjana Wijekoon"
    },
    {
      "name": "Roxana Ramirez Herrera"
    },
    {
      "name": "Sophia Bano"
    },
    {
      "name": "Peter McCulloch"
    },
    {
      "name": "Danail Stoyanov"
    },
    {
      "name": "Matthew J Clarkson"
    },
    {
      "name": "Enrico Costanza"
    },
    {
      "name": "Ann Blandford"
    },
    {
      "name": "Hani Joseph Marcus"
    },
    {
      "name": "CARES Evaluation Group"
    },
    {
      "name": "Mohammed Mustafa Adawi"
    },
    {
      "name": "Joao Paulo Almeida"
    },
    {
      "name": "Jensen Ang"
    },
    {
      "name": "Anouk Borg"
    },
    {
      "name": "Roman Bošnjak"
    },
    {
      "name": "Alexandros Boukas"
    },
    {
      "name": "Aswin Chari"
    },
    {
      "name": "Lekhaj C Daggubati"
    },
    {
      "name": "Neil L Dorward"
    },
    {
      "name": "Samuel Emerson"
    },
    {
      "name": "Laurence Johann Glancz"
    },
    {
      "name": "Paul Gwammache"
    },
    {
      "name": "John Gerrard Hanrahan"
    },
    {
      "name": "Joachim Starup-Hansen"
    },
    {
      "name": "Lauren Harris"
    },
    {
      "name": "Mark Hughes"
    },
    {
      "name": "Abhiney Jain"
    },
    {
      "name": "Adham M Khalafallah"
    },
    {
      "name": "Taufiq Khan"
    },
    {
      "name": "Muhammad Sohaib Khan"
    },
    {
      "name": "Ramez Wadie Kirollos"
    },
    {
      "name": "Angelos G Kolias"
    },
    {
      "name": "Jacek Kunicki"
    },
    {
      "name": "Ruth Lau"
    },
    {
      "name": "Juan Carlos Fernandez-Miranda"
    },
    {
      "name": "Ramesh Nair"
    },
    {
      "name": "Nicola Newall"
    },
    {
      "name": "Amr Nimer"
    },
    {
      "name": "Fabio Massimo Oddi"
    },
    {
      "name": "Piero Andrea Oppido"
    },
    {
      "name": "Piotr Paździora"
    },
    {
      "name": "Hafiz Muhammad Irfan Razzaq"
    },
    {
      "name": "Luis Alejandro Flores Salazar"
    },
    {
      "name": "Olabisi Sanusi"
    },
    {
      "name": "Thomas Santarius"
    },
    {
      "name": "Sebastian Senger"
    },
    {
      "name": "Jonathan Shapey"
    },
    {
      "name": "Nathan A Shlobin"
    },
    {
      "name": "Nicholas Wake Macmeikan Thomas"
    },
    {
      "name": "Sebastian Miguel Toescu"
    },
    {
      "name": "Georgios Tsermoulas"
    },
    {
      "name": "Juan Francisco Villalonga"
    },
    {
      "name": "Adam Williams"
    },
    {
      "name": "Nina Yoh"
    },
    {
      "name": "Brett E Youngerman"
    },
    {
      "name": "Gabriel Zada"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6fw2csqj5gw0k.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-74953-y",
  "fulltext_url": null,
  "id": "6fw2csqj5gw0k",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-74953-y",
  "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",
      "1702|Explainable Artificial Intelligence (XAI)"
    ]
  ],
  "openalex": "W7223446601",
  "pdf_url": "https://www.nature.com/articles/s41598-026-74953-y_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/6fw2csqj5gw0k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:33Z",
      "native_id": "10.1038/s41598-026-74953-y",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-74953-y"
    }
  ],
  "subjects": [],
  "title": "Exploring clinician interpretability of AI decision support systems for surgical anatomy recognition",
  "title_text": "Exploring clinician interpretability of AI decision support systems for surgical anatomy recognition",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:33Z",
  "url": "https://research.draconitic.com/paper/6fw2csqj5gw0k",
  "venue": "Scientific Reports",
  "versions": []
}