Navigating the AI Revolution: A New Chapter for the Diabetes and Metabolism Journal

Junghyun Noh

doi:10.4093/dmj.2026.0171

Articles

Page Path: HOME > Diabetes Metab J > Volume 50(2); 2026 > Article

Editorial Navigating the AI Revolution: A New Chapter for the Diabetes and Metabolism Journal: Junghyun Noh; Diabetes & Metabolism Journal 2026;50(2):253-254.
DOI: https://doi.org/10.4093/dmj.2026.0171
Published online: March 1, 2026

1,604 Views
2,147,483,677 Download

Division of Endocrinology and Metabolism, Department of Internal Medicine, Inje University Ilsan Paik Hospital, Goyang, Korea

Corresponding author: Junghyun Noh

Division of Endocrinology and Metabolism, Department of Internal Medicine, Inje University Ilsan Paik Hospital, 170 Juhwa-ro, Ilsanseo-gu, Goyang 10380, Korea E-mail: jhnoh@paik.ac.kr

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

prev next

Full Article

Download PDF

NOTES
REFERENCES
About this article

As I assume the role of editor-in-chief of the Diabetes and Metabolism Journal (DMJ), I am deeply honored to lead a journal with a strong tradition of advancing research of diabetes and metabolism. The recent acceleration of artificial intelligence (AI) technologies has begun to influence how scientific evidence is generated, evaluated, and shared. These changes bring new opportunities for academic publishing, while also raising important questions that require careful consideration by our journal and its editorial leadership.

Over the past several years, the use of AI tools in research and manuscript preparation has increased steadily. Large language models, including ChatGPT, Claude, and similar platforms, have progressed rapidly and are now able to assist with tasks such as drafting scientific text, exploring data, and preparing visual materials [1,2]. These tools can improve research efficiency and access, but their wider use has also increased the number of manuscripts that involve AI-generated or AI-assisted content, including in endocrinology and metabolism [3]. A survey conducted by Elsevier involving 3,234 researchers from 113 countries showed a marked increase in the use of AI tools in 2025 (58%) compared with 2024 (37%). This number is expected to rise significantly in the coming years [4]. Over half of respondents reported current time-saving benefits, and most anticipated greater efficiency gains in the coming years.

For DMJ, this development raises an important question: how can we preserve the standards of originality, scientific rigor, and trust that our readers and authors expect, while allowing appropriate and responsible uses of AI in scientific communication?

The expanding presence of AI-generated content raises a set of issues that warrant careful and systematic consideration. One concern relates to scientific integrity and originality, as AI-assisted text may contain inaccurate interpretations or unreliable references [5]. Another challenge lies in the increasing ability of advanced AI systems to produce fabricated figures, modify images, or generate synthetic datasets that can be difficult to distinguish from genuine research outputs [6]. In addition, the growing use of these tools prompts important questions about authorship, contributorship, and the appropriate disclosure of AI assistance in scientific work [7]. Finally, although AI can support the production of fluent and grammatically sound text, it does not replace the depth of scientific judgment, critical analysis, and domain expertise required for high-quality research [8].

In response, DMJ is developing a practical approach that seeks to balance innovation with scientific integrity. As methods for identifying AI-assisted content continue to evolve, we are evaluating tools that may assist the editorial process, particularly those designed to detect potential manipulation of data or images. In circumstances where questions persist, the journal may also request access to underlying data and detailed methodological documentation to facilitate independent assessment of the reported findings.

With immediate effect, DMJ asks authors to clearly disclose any and all use of AI-based tools during manuscript preparation, data analysis, or the development of figures. This disclosure should specify the tools used, describe how they were applied, and include confirmation that any AI-assisted content has been reviewed and validated by the authors for accuracy. The information should be provided either within the Methods section or as a dedicated ‘AI Assistance Statement’ and will be published alongside the article. This policy is intended to promote transparency and to help readers interpret published work appropriately.

Looking ahead, DMJ recognizes that well-conducted studies using AI-based methods may become more common. If a sufficient body of rigorous and well-documented work emerges, the journal may, at an appropriate time, consider organizing a dedicated forum or section to accommodate such contributions. The intent of any future initiative would be to provide a focused venue for studies that apply AI in a scientifically sound and transparent manner, including research related to drug development, risk assessment, predictive modeling, and precision medicine. This perspective reflects our view that AI, when used thoughtfully and responsibly, can complement traditional research methods and support meaningful advances in the care of patients with diabetes and metabolic diseases.

DMJ is also strengthening its internal capacity to address AI-related issues in scholarly publishing. This includes ongoing training for editors, reviewers, and journal staff that addresses the recognition of AI-assisted content, emerging forms of data and image manipulation, and the ethical considerations involved in evaluating research that incorporates AI-based methods. We also recognize that these challenges cannot be addressed by individual journals alone. DMJ therefore intends to collaborate with other journals and to participate in broader discussions on AI ethics in scientific publishing. Engagement with authors through guidance and educational activities will remain an important part of this effort.

The integration of AI into scientific research is likely to continue and, in many respects, offers clear benefits. DMJ’s goal is not to resist this change, but to help guide its responsible use within research and publishing. As we move forward, our core commitment remains unchanged: to publish methodologically sound, scientifically rigorous research that advances understanding and care in diabetes and metabolic disease. With the continued engagement of our editorial board, reviewers, authors, and readers, DMJ will strive to uphold the highest standards of scientific integrity while remaining open to thoughtful innovation.

NOTES

CONFLICTS OF INTEREST

Junghyun Noh has been an editor-in-chief of the Diabetes and Metabolism Journal since 2026. She was not involved in the review process of this article. Otherwise, there was no conflict of interest.

REFERENCES

1. Stokel-Walker C, Van Noorden R. What ChatGPT and generative AI mean for science. Nature 2023;614:214-6.Article PubMed PDF
2. Liebrenz M, Schleifer R, Buadze A, Bhugra D, Smith A. Generating scholarly content with ChatGPT: ethical challenges for medical publishing. Lancet Digit Health 2023;5:e105-6.Article PubMed
3. Vehi J, Mujahid O, Beneyto A, Contreras I. Generative artificial intelligence in diabetes healthcare. iScience 2025;28:113051.Article PubMed PMC
4. Elsevier. Researcher of the future: a confidence in research report. Available from: https://assets.ctfassets.net/o78em1y1w4i4/137SmnpRSP2mSuhDxtFdls/72a1777e8a72f3c60748956037f76433/Researcher-Of-The-Future.pdf (cited 2026 Feb 24).
5. Linardon J, Jarman HK, McClure Z, Anderson C, Liu C, Messer M. Influence of topic familiarity and prompt specificity on citation fabrication in mental health research using large language models: experimental study. JMIR Ment Health 2025;12:e80371.Article PubMed PMC
6. Taloni A, Scorcia V, Giannaccare G. Large language model advanced data analysis abuse to create a fake data set in medical research. JAMA Ophthalmol 2023;141:1174-5.Article PubMed PMC
7. Thorp HH. ChatGPT is fun, but not an author. Science 2023;379:313.Article PubMed PMC
8. Artificial intelligence is not a substitute for human intelligence. Nat Rev Psychol 2025;4:753-4.Article PDF

Figure & Data

References

Citations

Citations to this article as recorded by

PubReader
ePub Link

Cite this Article

Cite this Article: export Copy Download Format; Close

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

RIS — For EndNote, ProCite, RefWorks, and most other reference management software
BibTeX — For JabRef, BibDesk, and other BibTeX-specific software

Include:

Citation for the content below
Citation and abstract for the content below

Navigating the AI Revolution: A New Chapter for the Diabetes and Metabolism Journal

Diabetes Metab J. 2026;50(2):253-254. Published online March 1, 2026

DOI: https://doi.org/10.4093/dmj.2026.0171

XML Download

Navigating the AI Revolution: A New Chapter for the Diabetes and Metabolism Journal

About this article

Noh J. Navigating the AI Revolution: A New Chapter for the Diabetes and Metabolism Journal. Diabetes Metab J. 2026;50(2):253-254.

DOI: https://doi.org/10.4093/dmj.2026.0171.

Download citation ↓