@article{JMAI11323,
author = {Younan Li and Jingjing Wang and Qiyu Guo and Lianzhu He and Baitian Zhao and Yi Zhang and Guangjun Guan and Ran Xu and Jintao Liang and Ye Cao},
title = {Development of consensus-based rules for AI-assisted Clinical Trial Agreement review: a Delphi study},
journal = {Journal of Medical Artificial Intelligence},
volume = {9},
number = {0},
year = {2026},
keywords = {},
abstract = {Background: Clinical Trial Agreement (CTA) review is a critical bottleneck in trial initiation due to its complexity and reliance on specialized expertise. While artificial intelligence (AI) can enhance efficiency, the absence of standardized, expert-endorsed, and large language model (LLM)-utilizable rules limits its application. This study developed a consensus-based rule set for LLM-assisted CTA review within China’s regulatory context.Methods: A preliminary set of 74 rules (categorized into legal, financial, operational, and quality management domains) was drafted based on International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use-Good Clinical Practice (ICH-GCP), Chinese Good Clinical Practice (GCP) guidelines, institutional Standard Operating Procedures (SOPs), and expert interviews. A Delphi survey of three rounds was carried out with the participation of 23 Chinese CTA experts recruited via purposive and snowball sampling from diverse regions and institutions. Eligibility criteria included ≥3 years of CTA review/management experience and no conflicts of interest. A rule was approved only if it met both pre-defined criteria: (I) achieved high or medium consensus, determined by mean score, standard deviation, and coefficient of variation, and (II) received an importance rating of “critical” or “important” from at least 85% of experts. Anonymity was maintained through online questionnaires to minimize bias.Results: After three-round Delphi survey, high consensus was achieved on 76 rules (14 revised +2 new rules based on expert feedback). The panel showed strong engagement (response rate ≥95.65%), authority (composite reliability =0.89), and coordination (Kendall’s W =0.87–0.91, P},
issn = {2617-2496}, url = {https://jmai.amegroups.org/article/view/11323}
}