AIMC Topic: Editorial Policies

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Best Practices for Using AI Tools as an Author, Peer Reviewer, or Editor.

Journal of medical Internet research
The ethics of generative artificial intelligence (AI) use in scientific manuscript content creation has become a serious matter of concern in the scientific publishing community. Generative AI has computationally become capable of elaborating researc...

The Peer Review System: A Journal Editor's 30-Year Perspective.

Clinics in podiatric medicine and surgery
The peer review system has become the standard by which scientific articles are refereed. Unfortunately, even from its beginnings in the mid-1800s it has been fraught with difficulties. Potential reviewers are volunteers who may be inundated with req...

Let's be fair. What about an AI editor?

Accountability in research
Much of the current attention on artificial intelligence (AI)-based natural language processing (NLP) systems has focused on research ethics and integrity but neglects their roles in the editorial and peer-reviewing process. We argue that the academi...

Challenges for enforcing editorial policies on AI-generated papers.

Accountability in research
ChatGPT, a chatbot released by OpenAI in November 2022, has rocked academia with its capacity to generate papers "good enough" for academic journals. Major journals such as and professional societies such as the World Association of Medical Editors ...

Letter to editor: NLP systems such as ChatGPT cannot be listed as an author because these cannot fulfill widely adopted authorship criteria.

Accountability in research
This letter to the editor suggests adding a technical point to the new editorial policy expounded by Hosseini et al. on the mandatory disclosure of any use of natural language processing (NLP) systems, or generative AI, in writing scholarly publicati...

Recommendations for Reporting Machine Learning Analyses in Clinical Research.

Circulation. Cardiovascular quality and outcomes
Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of ...