Large language models (LLMs), a new family of tools to generate natural language, are likely to radically change how some aspects of healthcare are delivered.1 It is difficult to comprehend the pace at which change is already occurring. For example, ChatGPT, released by OpenAI in November 2022, represented a leap in the capability of artificial intelligence (AI).2 This was followed by the release of the Gemini family of models from Google and Claude-3 from Anthropic, as well as numerous other models with impressive capabilities. The aim of this analysis article is to illustrate how LLMs can already be used to improve healthcare based on their power to find and convey information. At the same time, we want to alert users to the pitfalls of relying on this form of AI: vigilance will be required.

The reflex to always turn to search engines is already built into how we work. We have all become aware of AI applications in speech recognition and automation to some extent.3 But LLMs take AI to a new level, enabling conversational analysis and synthesis of available knowledge at a scale and sophistication that, even by the admission of its engineers, is not fully understood.4

Of note, there is significant scope for LLMs to facilitate increased levels of shared decision-making (SDM). SDM is defined as ‘an approach where clinicians and patients share the best available evidence when faced with the task of making decisions and where patients are supported to consider options to achieve informed preferences’.5 In this article, we address the likely consequences of LLMs for SDM.

The basic building block of LLMs, such as ChatGPT or Gemini, is the ability to predict the probability of the ‘next word’ in a string of words.6 But this somewhat modest-sounding ability unfolds …