Mapping the Evolution of Artificial Intelligence in Medical Materials.

Sha Huang, Yang Jin, Xinting Hou, Zhengwei Huang, Jian Hu, Haojie Yu, Zhengfu He

Abstract read

In one paragraph

Article in ACS omega, 2025. 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

7 authors.

Sha HuangDepartment of Thoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China.

Yang JinState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China.

Xinting HouDepartment of Clinical Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China.

Zhengwei HuangDepartment of Thoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China.

Jian HuDepartment of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China.

Haojie YuState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China.ORCID https://orcid.org/0000-0002-7405-7881

Zhengfu HeDepartment of Thoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China.ORCID https://orcid.org/0000-0002-6747-7490

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The application of artificial intelligence (AI) in medical materials has been extensively explored, encompassing areas such as composition optimization, shape design, and manufacturing processes. However, a comprehensive study and analysis of the overall research landscape in this field remain scarce. To bridge this gap, this study employs bibliometric analysis to evaluate 108 publications from the Web of Science Core Collection database. The results reveal that research in this domain spans nearly two decades, with a notable surge in publications since 2018, probably driven by advancements in deep learning. Additionally, the study identifies significant challenges in AI for medical materials, such as clinical validation, de novo design, and precise manufacturing. These insights may provide guidance for future research in AI-enabled medical materials development.

Identifiers

PMID41018643

DOI10.1021/acsomega.5c06324

PMCPMC12461332

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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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.