{
  "abstract": "Although technical artificial intelligence (AI) development started as early as the 1950s,1 recent years have witnessed exponential growth in the volume of health-related AI research and early deployment of AI tools in clinical practice. The pace of advancement has been staggering and, for many, worrying.2 3 Misunderstandings about AI contribute heavily to the hype, therefore, there is a growing urgency for clinicians across all disciplines to have a general understanding of AI tools and their limitations. Familiarity with how AI tools are trained and tested is essential for interpreting their outputs in clinical decision-making. While there is undoubtedly an appetite for engaging with the technology, most clinicians have little or no experience in AI or computer science, and approaching the topic can be daunting for the uninitiated.4 5 Accessibility is a problem, as the field is littered with technical jargon with which end-users of AI tools are unlikely to be familiar. We aim to provide an educational foundation for clinicians to help them better understand the AI tools they encounter in clinical practice or research publications.",
  "authors": [
    {
      "affiliations": [
        "Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK"
      ],
      "name": "Ahmed Maiter"
    },
    {
      "affiliations": [
        "Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK"
      ],
      "name": "Samer Alabed"
    },
    {
      "affiliations": [
        "Center for Theoretical Neuroscience, Columbia University, New York, New York, USA"
      ],
      "name": "Genevera Allen"
    },
    {
      "affiliations": [
        "Departments of Biomedical Informatics, Biostatistics, and Epidemiology, and Cardiology, University of Missouri, Columbia, Missouri, USA"
      ],
      "name": "Fares Alahdab"
    }
  ],
  "title": "AI in healthcare: an introduction for clinicians",
  "uid": "0266ed27-66ad-5708-841d-7a2670d829a5"
}