Evidence map›Paper›PMID 37780993›Full record

ReviewThe Yale journal of biology and medicine2023

ChatGPT and the Future of Journal Reviews: A Feasibility Study.

Som Biswas, Dushyant Dobaria, Harris L Cohen

Abstract readCase ReportsReview
In one paragraph

Review in The Yale journal of biology and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 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

3 authors.

Som BiswasLe Bonheur Children's Hospital, The University of Tennessee Health Science Center, Memphis, TN, USA.
Dushyant DobariaLe Bonheur Children's Hospital, The University of Tennessee Health Science Center, Memphis, TN, USA.
Harris L CohenLe Bonheur Children's Hospital, The University of Tennessee Health Science Center, Memphis, TN, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing volume of research submissions to academic journals poses a significant challenge for traditional peer-review processes. To address this issue, this study explores the potential of employing ChatGPT, an advanced large language model (LLM), developed by OpenAI, as an artificial intelligence (AI) reviewer for academic journals. By leveraging the vast knowledge and natural language processing capabilities of ChatGPT, we hypothesize it may be possible to enhance the efficiency, consistency, and quality of the peer-review process. This research investigated key aspects of integrating ChatGPT into the journal review workflow. We compared the critical analysis of ChatGPT, acting as an AI reviewer, to human reviews for a single published article. Our methodological framework involved subjecting ChatGPT to an intricate examination, wherein its evaluative acumen was juxtaposed against human-authored reviews of a singular published article. As this is a feasibility study, one article was reviewed, which was a case report on scurvy. The entire article was used as an input into ChatGPT and commanded it to "Please perform a review of the following article and give points for revision." Since this was a case report with a limited word count the entire article could fit in one chat box. The output by ChatGPT was then compared with the comments by human reviewers. Key performance metrics, including precision and overall agreement, were judiciously and subjectively measured to portray the efficacy of ChatGPT as an AI reviewer in comparison to its human counterparts. The outcomes of this rigorous analysis unveiled compelling evidence regarding ChatGPT's performance as an AI reviewer. We demonstrated that ChatGPT's critical analyses aligned with those of human reviewers, as evidenced by the inter-rater agreement. Notably, ChatGPT exhibited commendable capability in identifying methodological flaws, articulating insightful feedback on theoretical frameworks, and gauging the overall contribution of the articles to their respective fields. While the integration of ChatGPT showcased immense promise, certain challenges and caveats surfaced. For example, ambiguities might present with complex research articles, leading to nuanced discrepancies between AI and human reviews. Also figures and images cannot be reviewed by ChatGPT. Lengthy articles need to be reviewed in parts by ChatGPT as the entire article will not fit in one chat/response. The benefits consist of reduction in time needed by journals to review the articles submitted to them, as well as an AI assistant to give a different perspective about the research papers other than the human reviewers. In conclusion, this research contributes a groundbreaking foundation for incorporating ChatGPT into the pantheon of journal reviewers. The delineated guidelines distill key insights into operationalizing ChatGPT as a proficient reviewer within academic journal frameworks, paving the way for a more efficient and insightful review process.

Indexed as

Artificial IntelligenceFeasibility StudiesHumanschatGPTjournal reviewreview

Identifiers

PMID37780993
PMCPMC10524821

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.