{
  "abstract": "Background Artificial intelligence (AI) and machine learning (ML) have shown immense potential in cardiology, leveraging data-driven insights to enhance diagnosis, treatment planning and patient care. This study presents a comprehensive evaluation of US Food and Drug Administration (FDA)-approved AI/ML devices in cardiology, analysing trends in clinical applications, regulatory pathways and evidence transparency.Methods FDA clearance summaries from the AI/ML medical device database were reviewed to identify cardiology-specific applications. Devices were categorised using the descriptive, diagnostic, predictive and prescriptive framework. Regulatory pathways, AI technologies and validation data were critically assessed.Results Of 1016 FDA-approved AI/ML devices, 277 (27.3%) had cardiology applications, predominantly for imaging (65.3%) and diagnostics (64.3%). Predictive and prescriptive tools constituted only 5.4% and 0.7%, respectively. Most devices (97.1%) were cleared via the 510(k) pathway, with 58.0% at risk of predicate creep. Quality of clinical evidence was limited, with only 3.2% of devices supported by high-quality trials. The type of AI technology was often underreported (58.8%).Conclusion While AI/ML technologies are reshaping cardiology, regulatory challenges and reporting transparency impede their optimal use. Strengthened regulatory frameworks, improved trial design and robust post-market surveillance are essential to ensure safety, efficacy and equity in the deployment of AI tools in cardiology.",
  "authors": [
    {
      "affiliations": [
        "Department of Cardiology, Queen Elizabeth Hospital, London, UK"
      ],
      "name": "Ahmed Hussain"
    },
    {
      "affiliations": [
        "Imperial College London Institute of Global Health Innovation, London, UK",
        "Department of Surgery and Cancer, Imperial College London, London, UK"
      ],
      "name": "Ahmad Guni"
    },
    {
      "affiliations": [
        "Imperial College London Institute of Global Health Innovation, London, UK",
        "Department of Surgery and Cancer, Imperial College London, London, UK"
      ],
      "name": "Rishikesh Gandhewar"
    },
    {
      "affiliations": [
        "The University of Manchester, Manchester, UK"
      ],
      "name": "John Warner-Levy"
    },
    {
      "affiliations": [
        "Imperial College London Institute of Global Health Innovation, London, UK"
      ],
      "name": "Alexander Davidson"
    },
    {
      "affiliations": [
        "London North West University Healthcare NHS Trust, London, UK"
      ],
      "name": "Kamal Shah"
    },
    {
      "affiliations": [
        "Imperial College London Institute of Global Health Innovation, London, UK",
        "Department of Surgery and Cancer, Imperial College London, London, UK"
      ],
      "name": "Ara Darzi"
    },
    {
      "affiliations": [
        "Imperial College London Institute of Global Health Innovation, London, UK",
        "Department of Surgery and Cancer, Imperial College London, London, UK"
      ],
      "name": "Hutan Ashrafian"
    }
  ],
  "title": "Is the FDA regulation of cardiology AI devices supporting cardiovascular innovation: a scoping review",
  "uid": "eebc2f86-17e4-50b1-be2c-7fa6dbae50b5"
}