夜雨聆风
- 数据基础；
- 系统配置；
- 与其他系统的集成；
- 供应商或服务提供商的管理安排；
- 用户操作规程；
- 审核工作流程；
- 使用边界与限制条件。
- 具备质疑AI输出的专业能力；
- 被正式授权接受或拒绝该输出；
- 有义务记录其决策依据。
- 预期用途是什么？
- 使用了哪些数据？
- 系统如何完成验证？是否包含充分的风险评估？
- 输出结果的适用边界在哪里？
- 如何监控系统性能？包括模型漂移（drifting）等问题？
- 谁负责审核？
- 谁对最终决策负有责任？
- 验证（Validation）；
- 数据完整性（Data Integrity）；
- 全生命周期控制；
- 人员培训；
- 变更管理；
- 质量监督。
When AI enters a regulated process, several things are at stake at once. The AI-enabled system (not just the model) must be fit for a clearly defined intended use. That includes the data foundation, configuration, integrations, supplier/service provider set-up, user procedure, review workflow, and limits of use. It must be validated in the context of where it is applied, monitored over time, and kept under change control. Only then can qualified people meaningfully review, approve, and defend the output.
Only Then Does the Signature Mean Something
This is why "human-in-the-loop" can be too weak as a control statement. A human is not a GMP control simply because they are present. The person must be qualified to challenge the output, authorized to accept or reject it, and required to document the basis for the decision.
AI can draft a procedure, summarize a deviation, compare records, or support process understanding. But once that output enters an SOP, CAPA, validation document, batch record, risk assessment, or release-related process, it is no longer just productivity support. It has become part of a regulated workflow.
The Discussion Must Move Beyond the Tool
What was the intended use?
What data were used?
How was the system validated, including proper risk assessment?
What are the output limits?
How is performance monitored, including drifting?
Who reviews it?
Who owns the final decision?
The maturing lesson is not that AI is too risky for GMP. Mature quality systems can benefit from AI. But AI scales the system it enters. Strong governance gets faster. Weak governance gets more convincing.
That is why AI governance in GMP cannot be reduced to a policy, a prompt library, or a vendor assessment. It must sit inside the pharmaceutical quality system, connected to validation, data integrity, lifecycle control, training, change management, and quality oversight.
The prompt will not be in the inspection room. The model will not carry quality unit responsibility. The company will. So, before asking whether AI can help, maybe the better question is "Can our regulated process defend what AI helped create?"
