Stidham Ryan W, Ghanem Louis R, Fletcher Joel G, Bruining David H
Division of Gastroenterology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, Michigan; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan.
Johnson & Johnson Innovative Medicine, Spring House, Pennsylvania.
Gastroenterology. 2025 Aug;169(3):432-443. doi: 10.1053/j.gastro.2025.02.039. Epub 2025 Mar 28.
Artificial intelligence (AI) will fundamentally improve how we perform clinical trials by addressing issues of standardizing disease scoring, improving the sensitivity and precision of activity and phenotype assessments, and automating laborious and time-consuming study functions. Progress in AI image analysis is quickly proving to replicate expert judgment in endoscopy, histology, and cross-sectional imaging with speed, reproducibility, and reduced bias. However, AI analytics offer the ability to quantify disease characteristics with more detail and precision than human experts. Large language models and generative AI are automating the collection of high-quality data from electronic records and improving our ability to predict patient outcomes. This narrative review will focus on AI tools available today, their expected implementation, and future-facing opportunities for AI to reimagine inflammatory bowel disease clinical trials.
人工智能（AI）将从根本上改善我们进行临床试验的方式，解决疾病评分标准化问题，提高活性和表型评估的灵敏度和精确度，并使繁琐且耗时的研究功能自动化。人工智能图像分析的进展很快证明，它能够在速度、可重复性和减少偏差方面复制内镜检查、组织学和横断面成像方面的专家判断。然而，人工智能分析能够比人类专家更详细、精确地量化疾病特征。大语言模型和生成式人工智能正在使从电子记录中收集高质量数据的过程自动化，并提高我们预测患者预后的能力。本叙述性综述将聚焦于当前可用的人工智能工具、它们的预期应用，以及人工智能重塑炎症性肠病临床试验的未来机遇。
