模拟滥用大型语言模型和临床信用系统的情况

IF 12.4 1区医学 Q1 HEALTH CARE SCIENCES & SERVICES

NPJ Digital Medicine Pub Date : 2024-11-11 DOI:10.1038/s41746-024-01306-2

James T. Anibal, Hannah B. Huth, Jasmine Gunkel, Susan K. Gregurick, Bradford J. Wood

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引用次数: 0

摘要

未来，大型语言模型（LLMs）可能会提高医疗服务的质量，但也存在滥用的风险。这些方法可能会被训练成通过涉及多模态数据（金融交易、互联网活动、社会行为和医疗保健信息）的不公正标准来分配资源。本研究表明，LLM 可能会偏向于集体/系统利益，而不是保护个人权利，并可能促进人工智能驱动的社会信用体系。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Simulated misuse of large language models and clinical credit systems

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Simulated misuse of large language models and clinical credit systems

In the future, large language models (LLMs) may enhance the delivery of healthcare, but there are risks of misuse. These methods may be trained to allocate resources via unjust criteria involving multimodal data - financial transactions, internet activity, social behaviors, and healthcare information. This study shows that LLMs may be biased in favor of collective/systemic benefit over the protection of individual rights and could facilitate AI-driven social credit systems.

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来源期刊

NPJ Digital Medicine Multiple-

CiteScore

25.10

自引率

3.30%

发文量

170

审稿时长

15 weeks

期刊介绍： npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.