Utilizing generative conversational artificial intelligence to create simulated patient encounters: a pilot study for anaesthesia training

Abstract

Abstract Purpose of the study Generative conversational artificial intelligence (AI) has huge potential to improve medical education. This pilot study evaluated the possibility of using a ‘no-code’ generative AI solution to create 2D and 3D virtual avatars, that trainee doctors can interact with to simulate patient encounters. Methods The platform ‘Convai’ was used to create a virtual patient avatar, with a custom backstory, to test the feasibility of this technique. The virtual patient model was set up to allow trainee anaesthetists to practice answering questions that patients’ may have about interscalene nerve blocks for open reduction and internal fixation surgery. This tool was provided to anaesthetists to receive their feedback and evaluate the feasibility of this approach. Results Fifteen anaesthetists were surveyed after using the tool. The tool had a median score [interquartile range (IQR)] of 9 [7–10] in terms of how intuitive and user-friendly it was, and 8 [7–10] in terms of accuracy in simulating patient responses and behaviour. Eighty-seven percent of respondents felt comfortable using the model. Conclusions By providing trainees with realistic scenarios, this technology allows trainees to practice answering patient questions regardless of actor availability, and indeed from home. Furthermore, the use of a ‘no-code’ platform allows clinicians to create customized training tools tailored to their medical specialties. While overall successful, this pilot study highlighted some of the current drawbacks and limitations of generative conversational AI, including the risk of outputting false information. Additional research and fine-tuning are required before generative conversational AI tools can act as a substitute for actors and peers.