Evidence map›Paper›PMID 42369686›Full record

ArticleSichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition2026

[Application and Reflection of Artificial Intelligence in Pharmacy Education].

Wencheng Liu, Tianguang Huang, Bowen Ke, Qiu Sun

Abstract readEnglish Abstract
In one paragraph

Article in Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Wencheng Liu( 610041)Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.ORCID 0009-0002-5679-6071
Tianguang Huang( 610041)Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.
Bowen Ke( 610041)Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.
Qiu Sun( 610041)Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.ORCID 0000-0001-5122-2226

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although the comprehensive, multi-level, and wide-ranging integration of artificial intelligence (AI) with pharmaceutical science has greatly advanced the development of the discipline, the majority of pharmaceutical professionals still have limited knowledge about the applications of AI in pharmacy. Particularly in recent years, the rapid development of generative AI technologies represented by large language models has introduced new paradigms for pharmaceutical knowledge acquisition, drug information mining, and intelligent education. Simultaneously, interdisciplinary talents in "AI + Pharmacy" play a crucial and irreplaceable role in the new drug development process.Faced with this reality, pharmaceutical educators should uphold the core philosophy of "AI empowerment, education first," actively disseminate cutting-edge AI concepts, and vigorously develop interdisciplinary advantages to address challenges in traditional pharmaceutical education. The core goal of cultivating innovative talents in "AI + Pharmacy" is to to nurture interdisciplinary professionals with solid pharmaceutical expertise, strong computational and data-driven thinking, and the ability to comprehensively apply AI technologies to solve complex problems in real-world research and application scenarios. To this end, his paper proposes a three-pillar "AI + Pharmacy" innovative talent cultivation model centered on "teaching restructuring, research-driven feedback, and faculty support," aiming to systematically promote comprehensive reforms in pharmaceutical education-from curriculum systems and teaching practices to faculty development. This approach seeks to gradually establish and continuously optimize a new paradigm for interdisciplinary pharmaceutical talent cultivation that aligns with contemporary demands, thereby providing sustainable talent support and innovative momentum for the overall advancement of pharmaceutical science and new drug development.

Indexed as

Artificial IntelligenceEducation, PharmacyCurriculumGenerative Artificial IntelligenceHumansArtificial intelligenceComprehensive talentsInterdisciplinaryPharmacy education

Identifiers

PMID42369686
PMCPMC13303512

What OpenQuestion holds

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Registered trials

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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.