Ethical Hazards of Large Language Models in Primary Care: A Clinician-Focused Update

Abstract

Large Language Models (LLMs) are transforming clinical workflows in primary care through capabilities like diagnostic support, clinical documentation, and simulated empathetic engagement. Yet, these advancements bring underappreciated ethical hazards that directly impact front-line physicians. Unlike general discussions of AI ethics, this chapter focuses on dilemmas arising with the use of LLMs in real-world primary care: questions of liability when LLM suggestions influence clinical decisions; risks to confidentiality when LLMs interact with protected health information; cognitive offloading that may erode diagnostic skills; disruptions in patient trust when LLMs simulate empathy; and the growing reality of patients turning to generative models as informal therapists. As LLMs are embedded into electronic medical records and clinical apps, physicians must become active ethical agents in how these tools are used. These challenges arise in contexts where regulatory frameworks lag behind technological deployment, placing responsibility squarely on individual clinicians to navigate uncertain ethical terrain. Drawing on current literature and real-world clinical challenges, this chapter proposes a clinician-focused ethical framework to guide the responsible use of LLMs in primary care. This framework addresses both immediate practical concerns—such as informed consent for LLM-assisted care and appropriate documentation of AI involvement—and longer-term questions about professional identity and diagnostic autonomy in an AI-augmented practice environment. The goal is not to demonize these powerful tools but to equip physicians with the necessary conceptual tools, awareness, and decision-making strategies for safe and ethical integration. Ultimately, the foundation of primary care—human judgment, presence, and trust—must remain at the center of clinical decision-making, even in an era augmented by LLMs.

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