Evidence map›Paper›PMID 39164055›Full record

ReviewJournal of Korean medical science2024

Evolution of Research Reporting Standards: Adapting to the Influence of Artificial Intelligence, Statistics Software, and Writing Tools.

Fatima Alnaimat, Salameh Al-Halaseh, Abdel Rahman Feras AlSamhori

Abstract readReview
In one paragraph

Review in Journal of Korean medical science, 2024. 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. AI hallucinations in academic writing: implications for research integrity.Naunyn-Schmiedeberg's archives of pharmacology · 2026
    Review
  2. Reporting the use of AI in horizon scanning: a brief communication on methodological standards.International journal of technology assessment in health care · 2026
    Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
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.

Fatima AlnaimatDivision of Rheumatology, Department of Internal Medicine, School of Medicine, University of Jordan, Amman, Jordan. f.naimat@ju.edu.jo.ORCID https://orcid.org/0000-0002-5574-2939
Salameh Al-HalasehDepartment of Internal Medicine, School of Medicine, University of Jordan, Amman, Jordan.ORCID https://orcid.org/0000-0002-2623-6885
Abdel Rahman Feras AlSamhoriDepartment of Internal Medicine, School of Medicine, University of Jordan, Amman, Jordan.ORCID https://orcid.org/0000-0002-2715-4320

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reporting standards are essential to health research as they improve accuracy and transparency. Over time, significant changes have occurred to the requirements for reporting research to ensure comprehensive and transparent reporting across a range of study domains and foster methodological rigor. The establishment of the Declaration of Helsinki, Consolidated Standards of Reporting Trials (CONSORT), Strengthening the Reporting of Observational Studies in Epidemiology (STROBE), and Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) are just a few of the historic initiatives that have increased research transparency. Through enhanced discoverability, statistical analysis facilitation, article quality enhancement, and language barrier reduction, artificial intelligence (AI)-in particular, large language models like ChatGPT-has transformed academic writing. However, problems with errors that could occur and the need for transparency while utilizing AI tools still exist. Modifying reporting rules to include AI-driven writing tools such as ChatGPT is ethically and practically challenging. In academic writing, precautions for truth, privacy, and responsibility are necessary due to concerns about biases, openness, data limits, and potential legal ramifications. The CONSORT-AI and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT)-AI Steering Group expands the CONSORT guidelines for AI clinical trials-new checklists like METRICS and CLEAR help to promote transparency in AI studies. Responsible usage of technology in research and writing software adoption requires interdisciplinary collaboration and ethical assessment. This study explores the impact of AI technologies, specifically ChatGPT, on past reporting standards and the need for revised guidelines for open, reproducible, and robust scientific publications.

Indexed as

Artificial IntelligenceSoftwareWritingHumansObservational Studies as TopicResearch DesignArtificial IntelligenceChatGPTData ReportingMachine LearningResearch

Identifiers

PMID39164055
PMCPMC11333804

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.