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人工智能、ChatGPT 及其他用于健康社会决定因素的大语言模型:现状与未来方向。

Artificial intelligence, ChatGPT, and other large language models for social determinants of health: Current state and future directions.

机构信息

Division of Pharmacy, Singapore General Hospital, Singapore, Singapore; SingHealth Duke-NUS Medicine Academic Clinical Programme, Singapore, Singapore.

MOHH Holdings (Singapore) Pte., Ltd., Singapore, Singapore; SingHealth Duke-NUS Family Medicine Academic Clinical Programme, Singapore, Singapore.

出版信息

Cell Rep Med. 2024 Jan 16;5(1):101356. doi: 10.1016/j.xcrm.2023.101356.

Abstract

This perspective highlights the importance of addressing social determinants of health (SDOH) in patient health outcomes and health inequity, a global problem exacerbated by the COVID-19 pandemic. We provide a broad discussion on current developments in digital health and artificial intelligence (AI), including large language models (LLMs), as transformative tools in addressing SDOH factors, offering new capabilities for disease surveillance and patient care. Simultaneously, we bring attention to challenges, such as data standardization, infrastructure limitations, digital literacy, and algorithmic bias, that could hinder equitable access to AI benefits. For LLMs, we highlight potential unique challenges and risks including environmental impact, unfair labor practices, inadvertent disinformation or "hallucinations," proliferation of bias, and infringement of copyrights. We propose the need for a multitiered approach to digital inclusion as an SDOH and the development of ethical and responsible AI practice frameworks globally and provide suggestions on bridging the gap from development to implementation of equitable AI technologies.

摘要

本观点强调了在患者健康结果和健康不平等方面解决健康的社会决定因素(SDOH)的重要性,这是一个由 COVID-19 大流行加剧的全球性问题。我们广泛讨论了数字健康和人工智能(AI)的当前发展,包括大型语言模型(LLM),它们是解决 SDOH 因素的变革性工具,为疾病监测和患者护理提供了新的能力。同时,我们注意到了一些挑战,如数据标准化、基础设施限制、数字素养和算法偏见,这些因素可能会阻碍公平地获得 AI 带来的好处。对于 LLM,我们强调了可能存在的独特挑战和风险,包括环境影响、不公平的劳工实践、无意中的虚假信息或“幻觉”、偏见的扩散以及侵犯版权。我们提出需要采取多层次的数字包容方法来解决 SDOH 问题,并在全球范围内制定道德和负责任的 AI 实践框架,并就弥合从开发到实施公平的 AI 技术的差距提出建议。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7693/10829781/cf23edcfb33d/gr1.jpg

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