Exploring patient trust in clinical advice from AI-driven LLMs like ChatGPT for self-diagnosis

Abstract

Trustworthy clinical advice is crucial but can be burdensome to obtain. Limited access and financial costs may lead people to self-diagnose. However, self-diagnosis requires considerable learning and can create risks when people pursue treatment without professional guidance. Large language models (LLMs) such as GPT-4 may offer a convenient yet risky alternative because they can produce inaccurate but convincing information. We therefore ask whether patients can trust clinical advice from AI-driven LLMs. We examined this question through a think-aloud observation in which a patient used GPT-4 for self-diagnosis while a doctor assessed its responses using professional expertise. We then conducted a semi-structured interview with the patient about their trust in the system. Our results show that patients may struggle to identify errors because they lack professional medical knowledge, even when GPT-4 provides advice that a doctor can recognize as false. Patients may develop some trust because GPT-4 explains its responses and acknowledges its limitations, but this trust remains uncertain because its advice can be unreliable. The doctor also reported that checking GPT-4's responses required more effort than making a diagnosis without it. Patients tend to trust doctors because educated and authorized professionals can provide effective guidance. This trust also develops through social connection, certification, institutional accountability, and professional rules. Doctors can adapt their questions, observe patients, and use different methods when patients cannot clearly describe their symptoms. An LLM, however, depends primarily on the information provided in a prompt and may overlook details that a doctor could identify during a clinical consultation. These findings raise questions about competence, responsibility, autonomy, and safety when LLMs are used for clinical advice.