Evidence map›Paper›PMID 37332008›Full record

ArticleAnnals of biomedical engineering2024

Revolutionary Potential of ChatGPT in Constructing Intelligent Clinical Decision Support Systems.

Zhiqiang Liao, Jian Wang, Zhuozheng Shi, Lintao Lu, Hitoshi Tabata

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 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Observational
  4. Article
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  6. Review
  7. Article
  8. Is ChatGPT worthy enough for provisioning clinical decision support?Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
  9. Article
  10. Reliability of large language models for advanced head and neck malignancies management: a comparison between ChatGPT 4 and Gemini Advanced.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024
    Article
  11. Reliability of large language models in managing odontogenic sinusitis clinical scenarios: a preliminary multidisciplinary evaluation.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024
    Article
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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.

Zhiqiang Liao *Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan. liao@bioxide.t.u-tokyo.ac.jp.ORCID http://orcid.org/0000-0002-4030-1364
Jian Wang *Department of Orthopaedics, Qilu Hospital of Shandong University, Jinan, 250012, People's Republic of China.
Zhuozheng ShiDepartment of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan.
Lintao LuDepartment of Orthopaedics, Qilu Hospital of Shandong University, Jinan, 250012, People's Republic of China. lulintao@mail.sdu.edu.cn.
Hitoshi TabataDepartment of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, Chatbot Generative Pre-trained Transformer (ChatGPT) is recognized as a promising clinical decision support system (CDSS) in the medical field owing to its advanced text analysis capabilities and interactive design. However, ChatGPT primarily focuses on learning text semantics rather than learning complex data structures and conducting real-time data analysis, which typically necessitate the development of intelligent CDSS employing specialized machine learning algorithms. Although ChatGPT cannot directly execute specific algorithms, it aids in algorithm design for intelligent CDSS at the textual level. In this study, besides discussing the types of CDSS and their relationship with ChatGPT, we mainly investigate the benefits and drawbacks of employing ChatGPT as an auxiliary design tool for intelligent CDSS. Our findings indicate that by collaborating with human expertise, ChatGPT has the potential to revolutionize the development of robust and effective intelligent CDSS.

Indexed as

Decision Support Systems, ClinicalAlgorithmsElectric Power SuppliesHumansMachine LearningSoftwareChatGPTClinical decision support systemsLarge language modelMachine learning

Identifiers

PMID37332008

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.