{
  "abstract": "Introduction: Minimally invasive surgery (MIS), including endovascular and robot-assisted procedures, is widely used for vascular pathologies but remains limited by technical complexity, cognitive workload, and postoperative complications. Artificial Intelligence (AI) may help address these challenges through predictive analysis, decision support, image analysis, and automation.\n\nMethods: A structured Google Scholar search was conducted from February 21, 2025, to January 1, 2026, and updated through September 2026. Peer-reviewed studies applying AI to vascular MIS were included, with additional targeted searches addressing clinical translation, deployment, regulation, and ethical considerations.\n\nResults: Thirty primary studies were analyzed, including eight on machine learning, nine on deep learning, four on natural language processing, and nine on robotics or reinforcement learning. Machine learning supported risk stratification and outcome prediction, deep learning enabled imaging and segmentation applications, natural language processing facilitated clinical information extraction, and robotic systems supported navigation and procedural assistance.\n\nDiscussion: AI demonstrates substantial potential across the vascular MIS pathway, but broader clinical adoption requires stronger validation, prospective multicenter studies, standardized reporting, health-economic evaluation, and careful consideration of regulatory, liability, and accessibility challenges.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School",
    "Chinese Medicine Guangdong Laboratory",
    "Department of General Surgery, Hebei Key Laboratory of Colorectal Cancer Precision Diagnosis and Treatment, The First Hospital of Hebei Medical University",
    "Institute of Biomedical Engineering, Boğaziçi University",
    "Medical Artificial Intelligence Lab, The First Hospital of Hebei Medical University",
    "Department of Oncology, The First Hospital of Hebei Medical University",
    "School of Mechanical, Electrical and Information Engineering, Shandong University",
    "Translational Medicine Research Center, Chongqing University Three Gorges Hospital, Chongqing University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
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        0,
        1
      ],
      "name": "Cenk Jiang"
    },
    {
      "affs": [
        0
      ],
      "name": "Bogdan Bortnovskii"
    },
    {
      "affs": [
        2
      ],
      "name": "Ijaz Gul"
    },
    {
      "affs": [
        3
      ],
      "name": "Mehmed Özkan"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Lian Zhang"
    },
    {
      "affs": [
        6
      ],
      "name": "Dongmei Yu"
    },
    {
      "affs": [
        7
      ],
      "name": "Vijay Pandey"
    },
    {
      "affs": [
        0
      ],
      "name": "Runming Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jian Wu"
    },
    {
      "affs": [
        1
      ],
      "name": "Peiwu Qin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8mrxngpch40nv.bib",
  "cited_by": 0,
  "doi": "10.3389/fcvm.2026.1966224",
  "fulltext_url": null,
  "id": "8mrxngpch40nv",
  "issns": [
    "2297-055X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fcvm.2026.1966224/full",
  "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",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7223178158",
  "pdf_url": "https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1966224/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/8mrxngpch40nv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:10Z",
      "native_id": "10.3389/fcvm.2026.1966224",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fcvm.2026.1966224/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Artificial intelligence for vascular minimally invasive surgery: techniques, clinical integration, and challenges",
  "title_text": "Artificial intelligence for vascular minimally invasive surgery: techniques, clinical integration, and challenges",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:10Z",
  "url": "https://research.draconitic.com/paper/8mrxngpch40nv",
  "venue": "Frontiers in Cardiovascular Medicine",
  "versions": []
}