用AI+人工分析临床试验数据，发现AI研究激增且中美主导。
Trends in AI and Human-AI Interaction in Clinical Trials -- A Hybrid Human-AI Exploration
- 结合GPT-5.5与人工审核，筛选并分类临床试验记录。
- 近年AI相关试验显著增长，机器学习、大语言模型等关键词频现。
- 适合关注医疗AI趋势或需规范临床试验报告的研究者。
本文分析了来自ClinicalTrials.gov注册库的记录，揭示了AI术语在临床试验中的时间趋势及地理分布特征。研究还探索了一种混合式人机协作方法，用于分析注册临床试验中的人机交互趋势。该流程采用前沿生成式AI模型（GPT-5.5）进行初步筛选，并由人工复核分类。结果表明，近年来与人工智能相关的试验数量明显上升，尤其体现在机器学习、深度学习、聊天机器人、GPT及大型语言模型等关键词的使用增加。从地理分布看，中国和美国是开展AI相关试验最多的国家，而意大利、法国、西班牙、英国和土耳其（Türkiye）也出现显著增长。在随机抽取的100条记录中，人类与AI分类器在识别非实质性使用AI的研究上达成良好一致，但在人机交互分类上一致性较低，尤其当医疗专业人员互动描述模糊或不足时。总体而言，混合人机筛查临床试验记录具有可行性，但更清晰的试验报告和更精准的人机交互定义将显著提升效果。
原文摘要 · Abstract (English)
This paper examines records retrieved from the ClinicalTrials.gov registry to characterize temporal trends in AI terminology and the geographical distribution of AI trials. The work also reports on an exploratory hybrid human-AI approach to analyzing human-AI interaction trends in registered clinical trials. The hybrid workflow comprised a frontier generative AI model (GPT-5.5) and human review to screen and categorize records returned by an AI-focused search. The findings indicate a marked increase in AI-related trials over time, with recent growth in references to machine learning, deep learning, chatbots, GPTs, and large language models. Geographically, China and the United States accounted for the largest numbers of AI-related trials, with notable recent increases in several other countries including Italy, France, Spain, the UK and Turkey (Türkiye). In a random sample of 100 records, human and AI classifiers showed good agreement in identifying studies not substantively using AI, but lower agreement in classifying human-AI interaction, particularly where health professional interaction was ambiguous or insufficiently described. Overall, the results suggest that hybrid human-AI screening of clinical trial records is potentially viable, but clearer trial reporting and more precise interaction definitions will benefit the process.
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