发表机构
Keele University; Manchester Metropolitan University（基尔大学; 曼彻斯特城市大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种多维分类框架，用于定义和分类临床试验中的人机交互，涵盖AI任务、关系、配置和人群，并通过15项试验验证了LLM辅助分类的潜力，强调人类判断的重要性。
AI 中文摘要
本文考察了临床试验中的人机交互（HAIIs），并提出了一种多维分类框架，该框架根据AI任务、人机关系、交互配置和交互人群对交互进行分类。我们定义了HAII，审视了现有分类法，并通过这一新颖的多维框架扩展了现有的分类方法。我们从先前报告的数据库中目的性抽样选取了15项临床试验。每项试验由两名人类评审员和六个大语言模型（LLM）分类器独立分类。所提出的分类提供了一种结构化方法，用于跨临床试验记录一致地识别、比较和综合人机交互。该框架旨在支持对AI相关临床试验进行更一致的比较和综合，并明确AI干预措施所涉及的不同形式的人类参与。结果表明了LLM辅助分类的潜力，同时强调了在试验记录不完整或模糊时人类判断的持续重要性。主要贡献是提出了一个多维框架，将AI任务、人机关系、交互配置和交互人群整合到一个专为临床试验记录设计的统一方法中。其意义在于支持更系统地识别、比较和综合人类与AI在临床试验中的交互方式。
英文摘要
This paper examines human-AI interactions (HAIIs) in clinical trials and presents a multidimensional categorisation framework that classifies interactions according to AI tasks, human-AI relationships, interaction configurations and interacting human groups. We define HAII, examine existing taxonomies and extend existing categorisation approaches through this novel multidimensional framework. We purposively sampled 15 clinical trials from a previously reported dataset. Each trial was independently categorised by two human reviewers and six large language model (LLM) classifiers. The proposed categorisation provides a structured method for the consistent identification, comparison and synthesis of human-AI interactions across clinical-trial records. The framework is intended to support more consistent comparison and synthesis of AI-related clinical trials and to make explicit the different forms of human involvement associated with AI interventions. The results demonstrate the potential for LLM-assisted categorisation while indicating the continuing importance of human judgement where trial records are incomplete or ambiguous. The principal contribution is a proposed multidimensional framework that brings together AI tasks, human-AI relationships, interaction configurations and interacting human groups within a single approach designed for clinical-trial records. Its significance lies in its potential to support more systematic identification, comparison and synthesis of how humans and AI interact in clinical trials.
Comments16 pages
