AI in peer review: Mapping promise and risk
A recent review in the International Journal of Medical Informatics explores how artificial intelligence is being used in scholarly peer review and what it means for research evaluation.
The review identifies two main roles for AI: assistive tools that help with manuscript screening and reviewer support, and autonomous systems that attempt to generate or evaluate reviews independently. The findings suggest that AI can improve efficiency and consistency, but current systems lack the domain expertise and ethical judgment required for independent decision-making.
The study also highlights risks including bias amplification, confidentiality concerns, and inconsistent governance across publishers. Its central message is that AI should be transparent, auditable, and used under human oversight.
What defines a high-quality peer review report
An editorial in Prehospital and Disaster Medicine outlines what makes a strong peer review report. It positions peer review as a system of quality improvement rather than a transactional step toward publication.
The article emphasizes scientific validity, clarity, accuracy, professionalism, staying within expertise, avoiding conflicts of interest, and keeping comments consistent with final recommendations.
Responsible use of research content in GenAI
A new STM framework addresses how generative AI should use scholarly research content. It highlights risks such as inaccurate citations, use of non-peer-reviewed material, and lack of transparency in sourcing.
The guidance emphasizes proper attribution, clear citation, prioritization of the version of record, and inclusion of corrections and retractions.
The rise of hallucinated citations in research
A Nature report examines the growing presence of AI-generated false citations in scientific literature. These citations range from entirely fabricated sources to realistic-looking combinations of real research elements.
The issue goes beyond traditional citation errors and introduces a new category of risk where references may not exist at all. Publishers and editors are responding with stricter screening processes, but detection remains challenging.
