Evidence map›Paper›PMID 40041903›Full record

ReviewActa pharmaceutica Sinica. B2025

The applications and advances of artificial intelligence in drug regulation: A global perspective.

Lixia Fu, Guoshu Jia, Zhenming Liu, Xiaocong Pang, Yimin Cui

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Acta pharmaceutica Sinica. B, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Lixia FuInstitute of Clinical Pharmacology, Peking University First Hospital, Beijing 100034, China.
Guoshu JiaInstitute of Clinical Pharmacology, Peking University First Hospital, Beijing 100034, China.
Zhenming LiuState Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China.
Xiaocong PangInstitute of Clinical Pharmacology, Peking University First Hospital, Beijing 100034, China.
Yimin CuiInstitute of Clinical Pharmacology, Peking University First Hospital, Beijing 100034, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has emerged as a transformative force in healthcare, with applications spanning diagnostics to drug development. However, its integration into drug regulation remains nascent, with varying degrees of adoption and implementation across different regulatory bodies worldwide. This review aims to provide a comprehensive overview of the current state of AI in drug regulation, encapsulating AI-related policies, initiatives, and its practical application in regulatory agencies globally. It further discusses the challenges and future prospects of AI in this field. The findings reveal that numerous agencies have launched action plans and initiatives to incorporate AI, aiming to streamline regulatory processes and enhance data-driven regulatory decision-making. Moreover, AI's deployment in safety surveillance, workflow optimization, and regulatory science research is expanding, highlighting its increasing impact on drug regulation. Nonetheless, key challenges persist, such as data quality and reliability, technical limitations, talent shortage and the absence of standards. The review concludes that interdisciplinary collaboration is crucial to harness AI's full potential in drug regulation and overcoming its current limitations. In the future, AI may become a pivotal catalyst in drug regulation, promising a new era of enhanced scrutiny, efficiency, and innovation that will benefit public health on a global scale.

Indexed as

Artificial intelligenceEmerging technologyMachine learningPharmacovigilanceRegulationRegulatory scienceSafety surveillance

Identifiers

PMID40041903
PMCPMC11873654

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.