Qualitative Health Research, Год журнала: 2023, Номер 33(13), С. 1135 - 1139
Опубликована: Окт. 28, 2023
Язык: Английский
Current Problems in Cardiology, Год журнала: 2024, Номер 49(3), С. 102387 - 102387
Опубликована: Янв. 5, 2024
Язык: Английский
Процитировано
22
Journal of Academic Ethics, Год журнала: 2025, Номер unknown
Опубликована: Фев. 11, 2025
Язык: Английский
Процитировано
3
Clinics and Practice, Год журнала: 2023, Номер 14(1), С. 89 - 105
Опубликована: Дек. 30, 2023
The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field nephrology academia. However, this advancement also given rise to ethical challenges, notably in scholarly writing. AI’s capacity automate labor-intensive tasks like literature reviews and data analysis created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives a range dilemmas that not only question authenticity contemporary endeavors but challenge credibility peer-review process integrity editorial oversight. Instances misconduct are highlighted, spanning from lesser-known journals reputable ones, even infiltrating graduate theses grant applications. subtle AI intrusion hints at systemic vulnerability within publishing domain, exacerbated by publish-or-perish mentality. solutions aimed mitigating employment academia include adoption sophisticated AI-driven plagiarism detection systems, robust augmentation an “AI scrutiny” phase, comprehensive training academics on usage, promotion culture transparency acknowledges role research. review underscores pressing need collaborative efforts among institutions foster environment application, thus preserving esteemed face rapid technological advancements. It makes plea rigorous research assess extent involvement literature, evaluate effectiveness AI-enhanced tools, understand long-term consequences utilization An example framework been proposed outline approach integrating Nephrology writing peer review. Using proactive initiatives evaluations, harmonious harnesses capabilities while upholding stringent standards can be envisioned.
Язык: Английский
Процитировано
37
Journal of Korean Medical Science, Год журнала: 2025, Номер 40(7)
Опубликована: Янв. 1, 2025
The rapid advancement of artificial intelligence (AI) has transformed various aspects scientific research, including academic publishing and peer review. In recent years, AI tools such as large language models have demonstrated their capability to streamline numerous tasks traditionally handled by human editors reviewers. These applications range from automated grammar checks plagiarism detection, format compliance, even preliminary assessment research significance. While substantially benefits the efficiency accuracy processes, its integration raises critical ethical methodological questions, particularly in lacks subtle understanding complex content that expertise provides, posing challenges evaluating novelty Additionally, there are risks associated with over-reliance on AI, potential biases algorithms, concerns related transparency, accountability, data privacy. This review evaluates perspectives within community integrating publishing. By exploring both AI's limitations, we aim offer practical recommendations ensure is used a supportive tool, supporting but not replacing expertise. Such guidelines essential for preserving integrity quality work while benefiting efficiencies editorial processes.
Язык: Английский
Процитировано
2
Journal of Healthcare Informatics Research, Год журнала: 2024, Номер 8(4), С. 658 - 711
Опубликована: Сен. 14, 2024
Язык: Английский
Процитировано
8
Journal of Business Analytics, Год журнала: 2025, Номер unknown, С. 1 - 14
Опубликована: Янв. 23, 2025
Язык: Английский
Процитировано
1
BMC Medicine, Год журнала: 2025, Номер 23(1)
Опубликована: Фев. 11, 2025
Abstract Background Generative artificial intelligence (GAI) has developed rapidly and been increasingly used in scholarly publishing, so it is urgent to examine guidelines for its usage. This cross-sectional study aims the coverage type of recommendations GAI usage among medical journals how these factors relate journal characteristics. Methods From SCImago Journal Rank (SJR) list medicine 2022, we generated two groups journals: top SJR ranked ( N = 200) random sample non-top 140). For each group, examined author reviewer across four categories: no guidelines, external only, own guidelines. We then calculated number by counting separately. Regression models relationship characteristics with Results A higher proportion provided compared (95.0% vs. 86.7%, P < 0.01). The had same median 5 on a scale 0 7 1 2 However, both lower percentages providing data analysis interpretation, having significantly percentage (32.5% 16.7%, 0.05). score was positively associated authors (all 0.01) reviewers journals. Conclusions Although most their or referenced some remained unspecified (e.g., whether AI can be interpretation). Additionally, scores were less likely provide indicating potential gap that warrants attention. Collaborative efforts are needed develop specific better guide reviewers.
Язык: Английский
Процитировано
1
Mayo Clinic Proceedings, Год журнала: 2024, Номер 99(1), С. 10 - 12
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
5
Stroke, Год журнала: 2024, Номер 55(10), С. 2573 - 2578
Опубликована: Сен. 3, 2024
Artificial intelligence (AI) large language models (LLMs) now produce human-like general text and images. LLMs' ability to generate persuasive scientific essays that undergo evaluation under traditional peer review has not been systematically studied. To measure perceptions of quality the nature authorship, we conducted a competitive essay contest in 2024 with both human AI participants. Human authors 4 distinct LLMs generated on controversial topics stroke care outcomes research. A panel
Язык: Английский
Процитировано
5
Accountability in Research, Год журнала: 2024, Номер unknown, С. 1 - 7
Опубликована: Сен. 18, 2024
The increasing use of AI tools, particularly large language models like ChatGPT, in academic research has raised significant questions about authorship and transparency. This commentary emphasizes the need for a standardized contributions taxonomy to clarify AI's role producing publishing outputs, ensuring ethical standards maintaining integrity.
Язык: Английский
Процитировано
4