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Introduction to the guiding principles on the use of large language models in regulatory science and for medicines regulatory activities issued by the European Medicines Agency
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Jian-hong PAN1, 2, Shang CAO3, Jun ZHAO1, 2
Chinese Journal of Clinical Pharmacology | 2026, 42(1) : 142 - 147
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Chinese Journal of Clinical Pharmacology | 2026, 42(1): 142-147
Drug Evaluation and Administration
Introduction to the guiding principles on the use of large language models in regulatory science and for medicines regulatory activities issued by the European Medicines Agency
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Jian-hong PAN1, 2, Shang CAO3, Jun ZHAO1, 2
Affiliations
  • 1.Center for Drug Evaluation, National Medical Products Administration, Beijing 100076, China
  • 2.State Key Laboratory of Drug Regulatory Science, Beijing 102629, China
  • 3.Yangtze River Delta Center for Drug Evaluation and Inspection of National Medical Products Administration, Shanghai 201210, China
Published: 2026-01-17 doi: 10.13699/j.cnki.1001-6821.2026.01.023
Outline
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With the advancement of artificial intelligence, the European Medicines Agency released " Guiding principles on the use of large language models in regulatory science and for medicines regulatory activities" in 2024, emphasizing the importance of the safe and responsible use of large language models. This guidance covers general ethical considerations, user principles and organizational principles, clearly outlining the potential of large language models in areas such as text processing assistance and data mining, while also warning of risks such as hallucinations, data privacy issues, and biased outputs. It proposes measures including continuous learning, risk monitoring and mechanism-building to address these challenges. Although China has not yet issued similar guidelines, large language models hold potential application prospects in areas such as assisting in the processing of review materials, formulating and revising guidance principles and identifying risks. At the same time, challenges related to decision interpretability, data bias, legal accountability and talent reserves remain. It is recommended that China’s regulatory authorities draw on international experience to construct an application framework tailored to national conditions as soon as possible, thereby promoting the integration of artificial intelligence technology with the field of drug regulation.

large language model  /  artificial intelligence  /  regulatory authorities
Jian-hong PAN, Shang CAO, Jun ZHAO. Introduction to the guiding principles on the use of large language models in regulatory science and for medicines regulatory activities issued by the European Medicines Agency[J]. Chinese Journal of Clinical Pharmacology, 2026 , 42 (1) : 142 -147 . DOI: 10.13699/j.cnki.1001-6821.2026.01.023
Year 2026 volume 42 Issue 1
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doi: 10.13699/j.cnki.1001-6821.2026.01.023
  • Receive Date:2025-07-01
  • Online Date:2026-08-06
  • Published:2026-01-17
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  • Received:2025-07-01
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Affiliations
    1.Center for Drug Evaluation, National Medical Products Administration, Beijing 100076, China
    2.State Key Laboratory of Drug Regulatory Science, Beijing 102629, China
    3.Yangtze River Delta Center for Drug Evaluation and Inspection of National Medical Products Administration, Shanghai 201210, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
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占总种数比例
Percentage of
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种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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