Ethical Considerations in Personal Health Large Language Models

Personal health large language models (PH-LLMs) have rapidly evolved from research prototypes into consumer-facing, data-linked systems that support symptom triage, medication questions, mental health check-ins, and longitudinal self-management. Their direct-to-consumer use without clinical oversight creates a distinct ethical risk profile that general artificial intelligence governance frameworks do not fully address. This viewpoint focuses on text-based, platform-mediated PH-LLMs and synthesizes PH-LLM-specific challenges across 6 domains: privacy, accuracy, equity, transparency, human-artificial intelligence interaction, and regulatory governance. These risks may be amplified by health literacy gaps, longitudinal data aggregation, persuasive conversational design, and fragmented oversight across the consumer-clinical boundary. Grounded in the 4 principles of biomedical ethics, we propose a governance framework that operationalizes beneficence, nonmaleficence, autonomy, and justice through design and deployment controls, including health literacy-aligned communication, crisis and pharmacological safeguards, hallucination mitigation, role disclosure, granular consent, fairness auditing, and accessible design. We further outline implementation mechanisms, including risk-tiered certification, tiered accountability, and postdeployment oversight through adverse-event reporting, transparency reporting, and independent safety evaluation. This framework is intended as an evidence-informed but partly anticipatory approach to governing PH-LLMs in personal health management.