AI evidence extraction

Nonhuman “Authors” and Implications for the Integrity of Scientific Publication and Medical Knowledge

Annette Flanagin, Kirsten Bibbins‐Domingo, Michael Berkwits, Stacy Christiansen · JAMA · 2023

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

3/4

Quality (LMQS)

Evidence

399

Citations

55.75

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1001/jama.2023.1344

Methodology & findings

Study design

Editorial commentary and policy analysis.

Main result

The editorial documents that "In January 2023, Nature reported on 2 preprints and 2 articles published in the science and health fields that included ChatGPT as a bylined author" and notes that "these articles and their nonhuman "authors" have already been indexed in PubMed and Google Scholar." The authors found that experiments with ChatGPT showed "text responses to questions, while mostly well written, are formulaic (which was not easily discernible), not up to date, false or fabricated, without accurate or complete references, and worse, with concocted nonexistent evidence for claims or statements it makes."

Reports effect sizes.

Research paradigm

Critical interpretivism; normative/regulatory analysis

Author conclusions

The authors conclude that "In this era of pervasive misinformation and mistrust, responsible use of AI language models and transparent reporting of how these tools are used in the creation of information and publication are vital to promote and protect the credibility and integrity of medical research and trust in medical knowledge." They also state that "AI technologies have existed for some time, will be further and faster developed, and will continue to be used in all stages of research and the dissemination of information, hopefully with innovative advances that offset any perils."

Open questions raised

The authors identify that the publishing community must address evolving risks and opportunities as "Transformative, disruptive technologies, like AI language models, create promise and opportunities as well as risks and threats for all involved in the scientific enterprise." They note that "Calls for journals to implement screening for AI-generated content will likely escalate," and that future EQUATOR Network reporting guidelines are under development for prognostic and diagnostic studies using AI and machine learning (STARD-AI and TRIPOD-AI).
The authors identify that the EQUATOR Network has "several other reporting guidelines in development for prognostic and diagnostic studies that use AI and machine learning, such as STARD-AI and TRIPOD-AI," suggesting ongoing work to address gaps in guidance for AI use in research.
The authors identify the need for screening tools to detect AI-generated content and note that "Calls for journals to implement screening for AI-generated content will likely escalate," though they acknowledge that "with large investments in further development, AI tools may be capable of evading any such screens."

Extracted from: pdfAgreement 79%

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