Sedano Rocio, Solitano Virginia, Vuyyuru Sudheer K, Yuan Yuhong, Hanžel Jurij, Ma Christopher, Nardone Olga Maria, Jairath Vipul
Division of Gastroenterology, Department of Medicine, Western University, London, ON, Canada.
Department of Epidemiology and Biostatistics, Western University, London, ON, Canada.
Therap Adv Gastroenterol. 2025 Feb 23;18:17562848251321915. doi: 10.1177/17562848251321915. eCollection 2025.
Integrating artificial intelligence (AI) into clinical trials for inflammatory bowel disease (IBD) has potential to be transformative to the field. This article explores how AI-driven technologies, including machine learning (ML), natural language processing, and predictive analytics, have the potential to enhance important aspects of IBD trials-from patient recruitment and trial design to data analysis and personalized treatment strategies. As AI advances, it has potential to improve long-standing challenges in trial efficiency, accuracy, and personalization with the goal of accelerating the discovery of novel therapies and improve outcomes for people living with IBD. AI can streamline multiple trial phases, from target identification and patient recruitment to data analysis and monitoring. By integrating multi-omics data, electronic health records, and imaging repositories, AI can uncover molecular targets and personalize trial strategies, ultimately expediting drug development. However, the adoption of AI in IBD clinical trials encounters significant challenges. These include technical barriers in data integration, ethical concerns regarding patient privacy, and regulatory issues related to AI validation standards. Additionally, AI models risk producing biased outcomes if training datasets lack diversity, potentially impacting underrepresented populations in clinical trials. Addressing these limitations requires standardized data formats, interdisciplinary collaboration, and robust ethical frameworks to ensure inclusivity and accuracy. Continued partnerships among clinicians, researchers, data scientists, and regulators will be essential to establish transparent, patient-centered AI frameworks. By overcoming these obstacles, AI has the potential to enhance the efficiency, equity, and efficacy of IBD clinical trials, ultimately benefiting patient care.
将人工智能（AI）整合到炎症性肠病（IBD）的临床试验中，有可能给该领域带来变革。本文探讨了人工智能驱动的技术，包括机器学习（ML）、自然语言处理和预测分析，如何有可能提升IBD试验的重要方面——从患者招募、试验设计到数据分析和个性化治疗策略。随着人工智能的发展，它有潜力改善试验效率、准确性和个性化方面长期存在的挑战，目标是加速新型疗法的发现并改善IBD患者的治疗效果。人工智能可以简化多个试验阶段，从靶点识别、患者招募到数据分析和监测。通过整合多组学数据、电子健康记录和影像库，人工智能可以发现分子靶点并使试验策略个性化，最终加速药物研发。然而，在IBD临床试验中采用人工智能面临重大挑战。这些挑战包括数据整合中的技术障碍、患者隐私方面的伦理问题以及与人工智能验证标准相关的监管问题。此外，如果训练数据集缺乏多样性，人工智能模型可能会产生有偏差的结果，这可能会影响临床试验中代表性不足的人群。解决这些限制需要标准化的数据格式、跨学科合作以及强大的伦理框架，以确保包容性和准确性。临床医生、研究人员、数据科学家和监管机构之间持续的合作对于建立透明的、以患者为中心的人工智能框架至关重要。通过克服这些障碍，人工智能有潜力提高IBD临床试验的效率、公平性和有效性，最终使患者护理受益。
