Acceptability of Health Care, Large Language Models, Perception, Self
Conditions
Keywords
Large language model, Artificial intelligence, Perception-based interventions, Public acceptance
Brief 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.
Detailed description
Owing to rapid advances in artificial intelligence, large language models (LLMs) are increasingly being used in a variety of clinical settings such as triage, disease diagnosis, treatment planning, and self-monitoring. Despite their potential, the use of LLMs remains restricted within healthcare settings due to lack of accuracy, coherence, and transparency and ethical concerns. Public perceptions such as perceived usefulness and risks play a crucial role in shaping their attitudes towards artificial intelligence that can either facilitate or hinder its adoption. Yet, to our knowledge, there is lack of awareness about perception-driven interventions in health care and no previous studies have examined whether public perceptions play a role in the acceptability of medical applications of LLMs. Hence, 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.
Interventions
OTHERPerception-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.
Sponsors
Peking University Third Hospital
Peking University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
SINGLE (Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* ≥18 years * Capable of completing an online survey * Agree to sign an informed consent form
Exclusion criteria
* Unable to answer questions or communicate * Not willing to participate in this study
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
China
Outcome results
None listed