Evidence map›Paper›PMID 37369944›Full record

ArticleAnnals of biomedical engineering2024

Advancing the Production of Clinical Medical Devices Through ChatGPT.

Siqi Li, Zheng Guo, Xuehui Zang

Abstract readLetter
PubMed Publisher
In one paragraph

Article in Annals of biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

3 authors.

Siqi Li *Advanced Research Center, GD Midea Equipment Co., Ltd, Foshan, 528000, China.
Zheng Guo *Orthopedics Department of The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, 528042, China. guozheng98@mail.sdu.edu.cn.ORCID http://orcid.org/0009-0005-5060-5571
Xuehui ZangOrthopedics Department of The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, 528042, China. lyzangxh@scut.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a recently popular large language model, Chatbot Generative Pre-trained Transformer (ChatGPT) is highly valued in the field of clinical medicine. Due to the limited understanding of the potential impact of ChatGPT on the manufacturing side of clinical medical devices, we aim to fill this gap through this article. We elucidate the classification of medical devices and explore the positive contributions of ChatGPT in various aspects of medical device design, optimization, and improvement. However, limitations such as the potential for misinterpretation of user intent, lack of personal experience, and the need for human supervision should be taken into consideration. Striking a balance between ChatGPT and human expertise can ensure the safety, quality, and compliance of medical devices. This work contributes to the advancement of ChatGPT in the medical device manufacturing industry and highlights the synergistic relationship between artificial intelligence and human involvement in healthcare.

Indexed as

Artificial IntelligenceSoftwareElectric Power SuppliesEquipment DesignHumansManufacturing IndustryChatGPTClinical medical devicesLarge language modelManufacturing engineering

Identifiers

PMID37369944

What OpenQuestion holds

Textmetadata
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.