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人工智能与医药产业的融合持续深化，从辅助诊断、药物研发到上市后监测，智能化技术在医疗各环节的应用探索不断拓展。临床数据管理作为临床试验中确保数据质量与完整性的重要环节，正逐步探索人工智能在数据采集、建库、清理、核查及锁库等流程中的潜在价值与应用路径。同时，eCOA等数字化数据来源日益普及，以及数据安全合规要求的持续强化，对数据治理、审计轨迹管理与风险管控提出了新的要求。行业需要在技术应用与合规实践之间寻求稳妥平衡，推动临床数据管理向更高效、更规范的方向发展。

The convergence of AI and the pharmaceutical industry continues to deepen, with intelligent technologies expanding their footprint across the healthcare spectrum—from diagnostic assistance and drug discovery through post-market surveillance. Clinical data management, pivotal to ensuring data quality and integrity in clinical trials, is increasingly exploring AI’s potential value and implementation pathways across data collection, database build, cleaning, verification, and lock processes. Meanwhile, the growing prevalence of digital data sources such as eCOA, alongside escalating data security and compliance requirements, is driving new imperatives for data governance, audit trail management, and risk mitigation. The industry needs to navigate a measured balance between technological innovation and regulatory compliance to advance clinical data management toward greater efficiency and standardization.