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人工智能估计大麻图像对人类影响的评级

Jacob T Borodovsky^1,2, Richard J Macatee³, Sarah M Preum^1,4

¹Center for Technology and Behavioral Health, Dartmouth Geisel School of Medicine, Lebanon, NH, USA.

medRxiv : the preprint server for health sciences

|December 3, 2025

概括

多模式生成人工智能 (MGAI) 可以接近人类对大麻图像的情绪反应,显示出公共卫生研究和在线营销监管的潜力.

科学领域:

计算社会科学计算社会科学
大麻监管科学大麻监管科学
人工智能在健康中的作用

背景情况:

在线大麻图像正在增加,可能会影响公众健康.
需要工具来评估这些图像对人口健康的影响.
人类对大麻暗示的情感反应已经得到了充分的证据.

研究的目的:

测试多模式生成人工智能 (MGAI) 是否可以复制大麻图像的人类影响评级.
评估MGAI作为分析大麻营销影响的可扩展工具的潜力.

主要方法:

创建了四种MGAI药物,每个药物都模拟了一个基于大麻施用方法 (bong,碗,关节/块,蒸发器) 的人类子组.
代理商使用零射击提示来对价值,兴奋和冲动等标准化图像进行评分.
用同等性测试和斯皮尔曼相关性,将MGAI评级与先前研究中的人类评级进行了比较.

主要成果:

MGAI评级与人类平均评级非常接近 (平均差异 = -0.31,SD = 1.23).
在MGAI和人类对价值 (r_s=0.55),兴奋 (r_s=0.34),和冲动 (r_s=0.56) 的评级之间观察到中等至高的相关性.
MGAI重现了人类数据中评级平均值和标准偏差之间的观察到的关系.

结论:

在接近人类大麻暗示反应模式方面,MGAI显示出前景.
进一步改进MGAI可能会为大麻监管科学提供一个有价值的工具.
MGAI可能有助于监管对在线大麻营销及其公共卫生影响的监管.

关键词:

影响评级影响评级人工智能的人工智能大麻大麻大麻大麻大麻大麻大麻大麻聊天GPT 聊天在GPT 聊天线索反应性线索反应性监管科学监管科学

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