  Trustworthy clinical advice is crucial but burdensome when seeking health
support from professionals. Inaccessibility and financial burdens present
obstacles to obtaining professional clinical advice, even when healthcare is
available. Consequently, individuals often resort to self-diagnosis, utilizing
medical materials to validate the health conditions of their families and
friends. However, the convenient method of self-diagnosis requires a commitment
to learning and is often not effective, presenting risks when individuals seek
self-care approaches or treatment strategies without professional guidance.
Artificial Intelligence (AI), supported by Large Language Models (LLM), may
become a powerful yet risky self-diagnosis tool for clinical advice due to the
hallucination of LLM, where it produces inaccurate yet deceiving information.
Thus, can we trust the clinical advice from AI-driven LLMs like ChatGPT like
ChatGPT4 for self-diagnosis? We examined this issue through a think-aloud
observation: a patient uses GPT4 for self-diagnosis and clinical advice while a
doctor assesses ChatGPT responses with their own expertise. After that, we
conducted a semi-structured interview with the patient to understand their
trust in AI-driven LLMs for clinical advice. we have concluded that the
confounding factors influencing a patient's trust revolve around their
competency-evaluation. Essentially, trust is equated with efficacy, which is
determined by whether decisions made based on the AI agent's clinical advice
and suggestion will effectively achieve the patient health goals. Patients tend
to trust doctors more than AI agents due to this strategy, believing that
educated, authorized doctors can provide effective medical guidance. This
competency-based trust also explains why patients often perceive more
experienced doctors as more trustworthy compared to less experienced ones.
