如何LLMs可以推进安全案例的摄入点,并从概念验证中获得见解
Hans-Joerg Roemming1, Manfred Hauben2,3, Wei Wannhoff2
1Merck Healthcare KGaA, Frankfurter Straße 250, Darmstadt, Hessen 64293, Germany.
Therapeutic advances in drug safety
|December 12, 2025
概括
大型语言模型 (LLM) 可以提高药品安全案例采集效率和数据的一致性. 这项技术提供了潜在的好处,同时解决了行业面临的实际挑战,以提高患者安全.
科学领域:
- 药监和药物安全 药监和药物安全
- 医疗保健中的人工智能
- 监管科学 监管科学
背景情况:
- 由于病例报告数量不断增加,制药公司面临越来越大的压力,需要加强药物监管流程.
- 维护数据完整性和患者安全是最重要的,因为需要更高的运营效率.
研究的目的:
- 探索大型语言模型 (LLM) 在制药安全案例采集工作流程中的潜在好处.
- 识别和讨论在行业中实施LLMs的实际挑战和考虑.
主要方法:
- 在安全案例采集中对LLM应用进行前性审查.
- 对概念验证 (PoC) 研究和已发表文献的分析.
- 估计数据提取和初始案例报告中的效率提升.
主要成果:
- 法律法规展示了提高数据提取一致性,减少偏差和提高整体效率的潜力.
- 一项PoC研究和文献综述表明,在现实场景中,实际应用性和效率增长.
结论:
- 法律学士学位为推进制药安全案例采纳流程提供了一个有前途的途径.
- 主要挑战包括投资回报率测量,专家参与,监管清晰度,系统集成和组织准备.
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