{
  "abstract": "Cardiovascular disease (CVD) remains the leading cause of death worldwide. While clinical scores provide modest predictive accuracy, the electrocardiogram (ECG) is rarely leveraged for prognosis. Artificial intelligence (AI) and deep learning now enable extraction of high-dimensional features from ECG waveforms for predicting cardiovascular outcomes. Our objective was to systematically evaluate the prognostic performance of AI-enabled ECG models for predicting all-cause mortality, cardiovascular mortality, and major adverse cardiovascular events (MACE). We searched PubMed, Cochrane CENTRAL, IEEE Xplore, and Google Scholar through November 2025 for studies using AI/ML models with 12-lead or single-lead ECGs to predict all-cause mortality, cardiovascular mortality, or MACE in adults (≥18 years old). Eligible studies included adults (≥18 years) using ECG as primary input. Two reviewers independently screened and extracted data. Narrative synthesis was conducted due to heterogeneity. Thirteen studies were included (~5.9 million patients). Eleven reported all-cause mortality (8 short-term: 30-day to 1-year), four reported cardiovascular mortality, and eight assessed MACE (4 composite MACE, 4 individual components). Models predominantly used convolutional neural networks [CNNs] on raw ECG waveforms. C-statistics ranged 0.70–0.89 for mortality prediction. Definitions and follow-up durations varied, limiting direct comparison. AI-ECG models demonstrate strong potential for predicting mortality and cardiovascular events with consistent performance and scalability. However, prospective validation and standardization are essential before clinical implementation.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Medicine, National Ribat University, Khartoum, Sudan"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Elhaitham Yasir Awad Musa"
    },
    {
      "name": "Amar Yasir Awad Musa"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ff537zf2j1v8f.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0360756",
  "fulltext_url": null,
  "id": "ff537zf2j1v8f",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0360756",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2705|ECG Monitoring and Analysis"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Medicine and health sciences/Cardiology/Cardiovascular medicine/Cardiovascular diseases/Cardiovascular disease risk"
    ],
    [
      "plos",
      "/Medicine and health sciences/Cardiology/Heart failure"
    ],
    [
      "plos",
      "/Medicine and health sciences/Cardiology/Myocardial infarction"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Cardiovascular diseases/Cardiovascular disease risk"
    ],
    [
      "plos",
      "/Research and analysis methods/Bioassays and physiological analysis/Electrophysiological techniques/Cardiac electrophysiology/Electrocardiography"
    ],
    [
      "plos",
      "/Research and analysis methods/Database and informatics methods/Database searching"
    ]
  ],
  "openalex": "W7222758030",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0360756&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/ff537zf2j1v8f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:23Z",
      "native_id": "10.1371/journal.pone.0360756",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0360756"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:cardiovascular diseases",
      "subfield": "cardiovascular"
    }
  ],
  "title": "Artificial intelligence-enhanced electrocardiography for predicting mortality and major adverse cardiovascular events: A systematic review and narrative synthesis",
  "title_text": "Artificial intelligence-enhanced electrocardiography for predicting mortality and major adverse cardiovascular events: A systematic review and narrative synthesis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:23Z",
  "url": "https://research.draconitic.com/paper/ff537zf2j1v8f",
  "venue": "PLOS One",
  "versions": []
}