JMIR Publications

ABSTRACT

Background:

With the rapid advancement of artificial intelligence (AI) technologies, AI-powered chatbots like ChatGPT have emerged as potential tools for various applications, including healthcare. However, ChatGPT is not specifically designed for healthcare purposes, and its use for self-diagnosis raises concerns regarding its adoption's potential risks and benefits. Users are increasingly inclined to employ ChatGPT for self-diagnosis, necessitating a deeper understanding of the factors driving this trend.

Objective:

This study aims to investigate the factors influencing users' perception of decision-making processes and intentions to use ChatGPT for self-diagnosis and to explore the implications of these findings for the safe and effective integration of AI chatbots in healthcare.

Methods:

A cross-sectional survey design was employed, and data were collected from 607 participants. The relationships between performance expectancy, risk-reward appraisal, decision-making, and intention to use ChatGPT for self-diagnosis were analyzed using partial least squares structural equation modeling (PLS-SEM).

Results:

Most respondents were willing to use ChatGPT for self-diagnosis (n=476). The model demonstrated satisfactory explanatory power, accounting for 52.4% of the variance in decision-making and 38.1% in the intent to use ChatGPT for self-diagnosis. The results supported all three hypotheses: higher performance expectancy of ChatGPT (β = 0.547, 95% CI [0.474, 0.620]) and positive risk-reward appraisals (β = 0.245, 95% CI [0.161, 0.325]) were positively associated with improved perception of decision-making outcomes among users, and enhanced perception of decision-making processes involving ChatGPT positively impacted users' intentions to utilize the technology for self-diagnosis (β = 0.565, 95% CI [0.498, 0.628]).

Conclusions:

Our research investigates factors influencing users' intentions to use ChatGPT for self-diagnosis and health-related purposes. Even though the technology is not specifically designed for healthcare, people are inclined to use ChatGPT in healthcare contexts. Instead of solely focusing on discouraging its use for healthcare purposes, we advocate for improving the technology and adapting it for suitable healthcare applications is crucial. Our study highlights the importance of collaboration among AI developers, healthcare providers, and policymakers in ensuring AI chatbots' safe and responsible use in healthcare. By understanding users' expectations and decision-making processes, we can develop AI chatbots like ChatGPT that are tailored to human needs, providing reliable and verified health information sources. This approach not only enhances healthcare accessibility but also improves health literacy and awareness. As the field of AI chatbots in healthcare continues to evolve, future research should explore the long-term effects of using AI chatbots for self-diagnosis and investigate their potential integration with other digital health interventions to optimize patient care and outcomes. By doing so, we can ensure that AI chatbots, including ChatGPT, are designed and implemented to safeguard users' well-being and support positive health outcomes in healthcare settings.