In the study, researchers from the New York University presented 392 people aged 18 and above with 10 patient questions and responses, with half of the responses generated by a human healthcare provider and the other half by OpenAI’s chatbot ChatGPT.
Participants were asked to identify the source of each response and rate their trust in the ChatGPT responses using a 5-point scale from completely untrustworthy to completely trustworthy.
The study, published in JMIR Medical Education, found people to have had limited ability while distinguishing between chatbot and human-generated responses.
On average, participants correctly identified chatbot responses 65.5% of the time and provider responses 65.1% of the time, with ranges of 49.0% to 85.7% for different questions.
Results remained consistent no matter the demographic categories of the respondents.
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The study also found participants mildly trust chatbots’ responses overall (3.4 average score), with lower trust when the health-related complexity of the task in question was higher. Logistical questions (e.g. scheduling appointments, insurance questions) had the highest trust rating (3.94 average score), followed by preventative care (e.g. vaccines, cancer screenings, 3.52 average score).
Diagnostic and treatment advice had the lowest trust ratings (scores 2.90 and 2.89, respectively).
According to the researchers, the study highlights the possibility that chatbots can assist in patient-provider communication particularly related to administrative tasks and common chronic disease management. Further research is needed around chatbots’ taking on more clinical roles, said the researchers from NYU Tandon School of Engineering and Grossman School of Medicine.
However, providers should remain cautious and exercise critical judgement when curating chatbot-generated advice due to the limitations and potential biases of AI models, they noted.
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