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New framework classifies human-AI interactions in clinical trials

By PulseAugur Editorial · [1 sources] ·

This paper introduces a new multidimensional framework for classifying human-AI interactions within clinical trials. The approach categorizes these interactions based on AI tasks, human-AI relationships, configuration, and involved human groups. Researchers applied this framework to 15 clinical trials, using both human reviewers and large language model (LLM) classifiers to categorize the interactions. The study highlights the potential of LLM-assisted categorization while emphasizing the continued necessity of human judgment for incomplete or ambiguous trial records. AI

IMPACT Provides a structured method for analyzing and comparing AI's role in clinical trials, potentially improving research synthesis.

RANK_REASON The item is an academic paper proposing a new classification framework for human-AI interactions in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework classifies human-AI interactions in clinical trials

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The item is an academic paper proposing a new classification framework for human-AI interactions in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sandra Woolley, Tim Collins, Khalid Khattak, Illia Chernomorets, Ariane Arevalo, Chris Richardson ·

    Defining and Categorising Human-AI Interactions in Clinical Trials: A Multidimensional Human-AI Classification Approach

    arXiv:2609.38559v1 Announce Type: new Abstract: 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…

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