Clinician voices on ethics of LLM integration in healthcare: a thematic analysis of ethical concerns and implications
Tala Mirzaei, Leila Amini, Pouyan Esmaeilzadeh
Abstract
OBJECTIVES: This study aimed to explain and categorize key ethical concerns about integrating large language models (LLMs) in healthcare, drawing particularly from the perspectives of clinicians in online discussions. MATERIALS AND METHODS: We analyzed 3049 posts and comments extracted from a self-identified clinician subreddit using unsupervised machine learning via Latent Dirichlet Allocation and a structured qualitative analysis methodology. RESULTS: Analysis uncovered 14 salient themes of ethical implications, which we further consolidated into 4 overarching domains reflecting ethical issues around various clinical applications of LLM in healthcare, LLM coding, algorithm, and data governance, LLM's role in health equity and the distribution of public health services, and the relationship between users (human) and LLM systems (machine). DISCUSSION: Mapping themes to ethical frameworks in literature illustrated multifaceted issues covering transparent LLM decisions, fairness, privacy, access disparities, user experiences, and reliability. CONCLUSION: This study emphasizes the need for ongoing ethical review from stakeholders to ensure responsible innovation and advocates for tailored governance to enhance LLM use in healthcare, aiming to improve clinical outcomes ethically and effectively.