Evidence map›Paper›PMID 41685169›Full record

ReviewActa pharmaceutica Sinica. B2026

Applications of AI/ML in accelerating the development of pulmonary drug delivery system.

Junhuang Jiang, Ziling Zhou, Tingting Peng, Zhengwei Huang, Xin Pan, Chuanbin Wu

Abstract readReview
In one paragraph

Review in Acta pharmaceutica Sinica. B, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

6 authors.

Junhuang JiangState Key Laboratory of Bioactive Molecules and Druggability Assessment, Guangdong Basic Research Center of Excellence for Natural Bioactive Molecules and Discovery of Innovative Drugs, College of Pharmacy, Jinan University, Guangzhou 511443, China.
Ziling ZhouState Key Laboratory of Bioactive Molecules and Druggability Assessment, Guangdong Basic Research Center of Excellence for Natural Bioactive Molecules and Discovery of Innovative Drugs, College of Pharmacy, Jinan University, Guangzhou 511443, China.
Tingting PengState Key Laboratory of Bioactive Molecules and Druggability Assessment, Guangdong Basic Research Center of Excellence for Natural Bioactive Molecules and Discovery of Innovative Drugs, College of Pharmacy, Jinan University, Guangzhou 511443, China.
Zhengwei HuangState Key Laboratory of Bioactive Molecules and Druggability Assessment, Guangdong Basic Research Center of Excellence for Natural Bioactive Molecules and Discovery of Innovative Drugs, College of Pharmacy, Jinan University, Guangzhou 511443, China.
Xin PanSchool of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510275, China.
Chuanbin WuState Key Laboratory of Bioactive Molecules and Druggability Assessment, Guangdong Basic Research Center of Excellence for Natural Bioactive Molecules and Discovery of Innovative Drugs, College of Pharmacy, Jinan University, Guangzhou 511443, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is a transformative technique for drug development, and it has been widely applied in pharmaceutical industry and academia. Pulmonary drug delivery systems (PDDS) are preferred for treating respiratory diseases due to their ability to provide localized and rapid action with fewer side effects. The integration of AI and Machine Learning (ML) has significantly accelerated the development of PDDS by enhancing both respiratory disease detection, and different stages during PDDS development. This paper provides an overview of the present landscape by literature analysis of the key areas of research. This review first introduces the fundamental principles of AI/ML and how they are applied in respiratory disease detection and diagnostics, highlighting FDA-approved software used in this field. Furthermore, we examine the role of AI in different stages during the development of PDDS, from identifying novel drug candidates to optimizing formulations and drug delivery mechanisms. The review also discusses regulatory and ethical considerations, along with existing challenges during AI-driven PDDS development. By addressing these key aspects, we provide insights into the revolutionary potential of AI/ML in advancing pulmonary drug delivery and improving therapeutic outcomes.

Indexed as

Artificial intelligenceArtificial neural networksMachine learningPulmonary drug delivery systemsRespiratory disease detection and diagnosticsRespiratory diseases

Identifiers

PMID41685169
PMCPMC12891894

What Socratic 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 Socratic graph.