Evidence map›Paper›PMID 37208451›Full record

ArticleAnnals of biomedical engineering2023

Can ChatGPT be Trusted for Consulting? Uncovering Doctor's Perceptions Using Deep Learning Techniques.

S V Praveen, Vajratiya Vajrobol

Abstract readLetter
PubMed Publisher
In one paragraph

Article in Annals of biomedical engineering, 2023. 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.

  1. Article
  2. Article
  3. Examining otolaryngologists' attitudes towards large language models (LLMs) such as ChatGPT: a comprehensive deep learning analysis.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
  4. Article
  5. Article
  6. 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

2 authors.

S V PraveenDepartment of Analytics, Xavier Institute of Management and Entrepreneurship, Bangalore, India. praveenscissci@gmail.com.ORCID http://orcid.org/0000-0002-1450-8839
Vajratiya VajrobolInstitute of Informatics and Communication, University of Delhi-South Campus, New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the introduction of ChatGPT by OpenAI in late 2022, the question of whether doctors can employ it for consultation has been a subject of debate. ChatGPT is a deep learning model trained on a vast dataset, but concerns about the reliability of its output have been a subject of debate in recent times. In this article, we have employed cutting-edge bidirectional encoder representations from transformers (BERT) sentiment analysis and topic modeling techniques to comprehend doctors' attitudes toward using ChatGPT in consultation.

Indexed as

Deep LearningElectric Power SuppliesReferral and ConsultationReproducibility of ResultsArtificial IntelligenceChatGPTConsultingDeep learningDoctor

Identifiers

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.