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TRUVACE RECORD VERSION record: TRV-2026-0440 version: 1 kind: certified reason: Certified into the record timestamp: 2026-07-20T10:52:11.264132Z status: published lens: p_space sector: health headline: Ethical and regulatory challenges of large language models in medicine dek: With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergent abilities of LLMs differentiate them substantially from other artificial intelligence (AI) models and natural language processing techniques used, necessitating a nuanced understanding of LLM ethics. In this Viewpoint, we highlight ethical concerns stemming from th… gain_title: (none) problem_title: Integration of large language models into medical practice creates ethical and regulatory risks including compromised data privacy and rights of use, unclear data provenance, and intellectual property contamination. trace_subject: (none) gain_reading: (none) gain_evidence: (none) problem_reading: Integration of large language models into medical practice creates ethical and regulatory risks including compromised data privacy and rights of use, unclear data provenance, and intellectual property contamination. problem_evidence: data privacy and rights of use | responsible integration of LLMs into medical practice quick_read: A Viewpoint published April 23, 2024 in The Lancet Digital Health examines ethical and regulatory challenges of large language models in medicine, arguing their architecture and emergent abilities set them apart from prior AI and NLP tools. The piece matters because it identifies unresolved governance gaps for clinical deployment, including who owns and may use medical data, where training data comes from, and how IP contamination occurs, while acknowledging that no comprehensive mitigation framework yet exists for responsible integration. limitation: tag: Evidence-backed problem key_points: Viewpoint focuses on large language models differentiated by technical architecture and emergent abilities from other AI and NLP techniques | Authors frame risks from three stakeholder perspectives: users, developers, and regulators | Specific risk domains named are data privacy and rights of use, data provenance, and intellectual property contamination | Authors argue broad applications and plasticity of LLMs increase ethical complexity in medical settings rundown: The authors note rapid growth of interest in and use of LLMs across industries, but argue medicine presents crucial and profound ethical concerns due to LLMs' unique architecture and purported emergent abilities. They organize concerns around users, developers, and regulators, and call for a comprehensive framework and mitigating strategies to align LLM use with ethical principles and safeguard against societal risks. sources: - peer_reviewed | The Lancet Digital Health | https://doi.org/10.1016/s2589-7500(24)00061-x | 2024-04-23 prev: 0000000000000000000000000000000000000000000000000000000000000000
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