{
  "abstract": "Atrial fibrillation (AF) is frequently asymptomatic and often remains undetected until complications arise. Although artificial intelligence (AI)-enabled electrocardiography (ECG) can predict incident AF from sinus rhythm ECGs, its influence on physician risk assessment in simulated clinical settings remains uncertain. We developed a deep learning model to predict multi-day AF risk using non-AF 12-lead ECGs. For ECG-labeled outcomes, the model achieved an AUROC of 0.79 in the internal EUMC cohort and 0.74 in the external BIDMC cohort. For Holter-labeled outcomes, AUROC values reached 0.87 in the internal EUMC subset and 0.75 in the prospective PROVISION-AF cohort. To assess decision-support utility, a multinational survey of 70 physicians evaluated how AI-derived risk estimates influenced physician risk assessment and follow-up decisions in structured simulated cases. AI assistance significantly improved physicians’ AF risk discrimination (AUROC 0.573 to 0.650) and negative predictive value (0.764 to 0.839), with significant net reclassification improvement for non-AF cases (NRI 0.127, p < 0.001). Performance gains were most notable among non-electrophysiologist cardiologists. In conclusion, AI-derived risk estimates improved physician risk discrimination in a structured simulated survey, particularly in non-specialist settings, supporting their potential role as a digital decision-support tool. Further real-world implementation studies are needed to determine whether these effects translate into improved clinical outcomes or healthcare efficiency.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Cardiology, College of Medicine, Ewha Womans University, Seoul, Republic of Korea",
    "Department of Urology, College of Medicine, Ewha Womans University Medical Center, SYNERGY AI, Ewha Womans University Mokdong Hospital, Seoul, Republic of Korea",
    "Clinical Trial Center, Ewha Womans University Medical Center, Seoul, Republic of Korea",
    "Division of Cardiology, Department of Internal Medicine, Korea University College of Medicine and Korea University Anam Hospital, Seoul, Republic of Korea",
    "Division of Cardiology, Department of Internal Medicine, Korea University College of Medicine and Korea University Guro Hospital, Seoul, Republic of Korea",
    "Division of Cardiology, Department of Internal Medicine, Korea University College of Medicine and Korea University Ansan Hospital, Seoul, Ansan, Republic of Korea",
    "Division of Cardiology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea",
    "Division of Cardiology, Department of Internal Medicine, Chonnam National University Hospital, Gwangju, Republic of Korea",
    "Division of Cardiology, Department of Internal Medicine, Kangbuk Samsung Hospital, Seoul, Republic of Korea",
    "Acryl 11F, Seoul, Korea",
    "KAIST (Korea Advanced Institute of Science and Technology), Daejeon, Korea"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 22,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yeji Kim"
    },
    {
      "affs": [
        1
      ],
      "name": "Bogeun Kim"
    },
    {
      "affs": [
        1
      ],
      "name": "Jaeung Yoon"
    },
    {
      "affs": [
        0
      ],
      "name": "Jeongmin Kang"
    },
    {
      "affs": [
        0
      ],
      "name": "Seung Woo Lee"
    },
    {
      "affs": [
        0
      ],
      "name": "Moon-Hyun Kim"
    },
    {
      "affs": [
        0
      ],
      "name": "Dong-Hyeok Kim"
    },
    {
      "affs": [
        2
      ],
      "name": "Hye Ah Lee"
    },
    {
      "affs": [
        3
      ],
      "name": "Jaemin Shim"
    },
    {
      "affs": [
        4
      ],
      "name": "Dae-In Lee"
    },
    {
      "affs": [
        4
      ],
      "name": "Seung-Young Roh"
    },
    {
      "affs": [
        5
      ],
      "name": "Seung Young Shin"
    },
    {
      "affs": [
        6
      ],
      "name": "Sung Hwa Choi"
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    {
      "affs": [
        6
      ],
      "name": "Yae Min Park"
    },
    {
      "affs": [
        7
      ],
      "name": "Ki Hong Lee"
    },
    {
      "affs": [
        7
      ],
      "name": "Yoo Ri Kim"
    },
    {
      "affs": [
        8
      ],
      "name": "Min Kim"
    },
    {
      "affs": [
        9
      ],
      "name": "Wei-Jin Park"
    },
    {
      "affs": [
        10
      ],
      "name": "Hyeon Yeo"
    },
    {
      "affs": [
        10
      ],
      "name": "Jungwon Choi"
    },
    {
      "affs": [
        1
      ],
      "name": "Tae Young Shin"
    },
    {
      "affs": [
        0
      ],
      "name": "Junbeom Park"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9a842421nbrnc.bib",
  "cited_by": 0,
  "doi": "10.1038/s41746-026-02950-6",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13737093/",
  "id": "9a842421nbrnc",
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  "landing_url": "https://doi.org/10.1038/s41746-026-02950-6",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
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      "2705|Atrial Fibrillation Management and Outcomes"
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  "pdf_url": "https://www.nature.com/articles/s41746-026-02950-6_reference.pdf",
  "pmcid": "PMC13737093",
  "pmid": "42443369",
  "published": "2026-07-13",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/9a842421nbrnc.ris",
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      "url": "https://doi.org/10.1038/s41746-026-02950-6"
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      "subfield": "cardiovascular"
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  "title": "Impact of an AI algorithm for multi-day prediction of incident atrial fibrillation on clinical decision-making: PROVISION-AF study",
  "title_text": "Impact of an AI algorithm for multi-day prediction of incident atrial fibrillation on clinical decision-making: PROVISION-AF study",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:22:23Z",
  "url": "https://research.draconitic.com/paper/9a842421nbrnc",
  "venue": "NPJ Digital Medicine",
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