Effect of Perception-based Interventions on Public Acceptance of Using Large Language Models in Medicine

Condition(s) studied

Investigational drug(s) / intervention(s)

Perception-based interventions: Participants allocated to the intervention group received perception-based interventions. Interventions for Groups 1-3 were perceived benefits of LLMs in medicine, perceived racial bias in LLMs in medicine, and perceived ethical conflicts in LLMs in medicine, respectively.

Study summary

Large language models (LLMs) show promise in medicine, but concerns about their accuracy, coherence, transparency, and ethics remain. To date, public perceptions on using LLMs in medicine and whether they play a role in the acceptability of health care applications of LLMs are not yet fully understood. This study aims to investigate public perceptions on using LLMs in medicine and if interventions for perceptions affect the acceptability of health care applications of LLMs.

Eligibility

Primary outcome measure(s)

• Number of participants who will change their attitudes towards medical applications of large language models — Through study completion, an average of 1 year
  Public acceptance of applying large language models to medicine will be categorized into yes, not sure, and no, which will be collected before perception-based interventions and after interventions.

Trial sites (1)

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