Ian A Scott, Sandeep Reddy, Tanya Kelly, Tim Miller, Anton van der Vegt
<p>Generative artificial intelligence (GenAI) is any computer system capable of generating text, images, or other types of content, often in response to a prompt or question entered through a chat interface. GenAI comprises large language models (LLMs) and other general-purpose foundation models powered mostly by generative pre-trained transformer (GPT) deep learning technology. Compared with traditional AI models using single data modalities for specific classification or prediction tasks, GenAI comprises task-agnostic, increasingly multimodal models that learn shared representations of different data types and, using suitable prompts, may perform never-before-seen tasks.<span><sup>1</sup></span> GenAI tools (also termed solutions or applications) are compelling because, unlike traditional AI, they are conversant, interacting directly with humans and generating human-like responses to prompts. These tools, in the form of ChatGPT and other GenAI chatbots, have very quickly captured the interest of researchers, clinicians and industry. Anecdotally, certain GenAI tools, such as ambient AI scribes and assistants, are already being used in many practice areas.<span><sup>2, 3</sup></span> In the UK, one in five general practitioners now routinely use GenAI for various tasks.<span><sup>4</sup></span> At the time of submission, this rapid uptake was occurring with little guidance on what use cases (tasks or clinical indications) are most amenable to GenAI, how GenAI tools intended for clinical practice should be used, evaluated and governed, and how to safeguard reliability, safety, privacy, and consent.</p><p>In addressing these issues, we undertook a narrative review of existing literature, and using this evidence, we propose a phased, risk-tiered approach to implementing GenAI tools, discuss risks and mitigations, and consider factors likely to influence adoption of GenAI by both clinicians and health services. Although GenAI encompasses both text and image generation, this review primarily focuses on text-based applications in clinical practice, with image-related applications limited to report generation rather than image generation. Box 1 contains a glossary of terms used when describing GenAI.</p><p>We searched PubMed and Google Scholar for articles published between 1 January 2022 and 31 August 2024 using search terms “generative AI”, “large language models”, “clinical practice” or “health care”. We focused on review articles and grouped them into key application domains to inform our implementation framework: clinical documentation (16), operational efficiency (20), patient safety (11), clinical decision making (42), and patient self-care (4). Seven reviews covering all these domains were also retrieved.<span><sup>5-11</sup></span> From these reviews, we extracted references outlining the problem(s) being addressed and exemplars of implemented GenAI tools used to solve them. We noted considerable heterogeneity in study design and methodological
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