Cocozza S, Palma G. Of editorial processes, AI models, and medical literature: the Magnetic Resonance Audiometry experiment.
Eur Radiol 2024:10.1007/s00330-024-10668-w. [PMID:
38451324 DOI:
10.1007/s00330-024-10668-w]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/24/2024] [Accepted: 02/01/2024] [Indexed: 03/08/2024]
Abstract
The potential of artificial intelligence (AI) in the field of medical research is unquestionable. Nevertheless, the scientific community has raised several concerns about a possible fraudulent use of these tools that might be used to generate inaccurate or, in extreme cases, erroneous messages that could find their way into the literature. In this experiment, we asked a generative AI program to write a technical report on a non-existing Magnetic Resonance Imaging technique called Magnetic Resonance Audiometry, receiving in return a full seemingly technically sound report, substantiated by equations and references. We have submitted this report to an international peer-reviewed indexed journal, passing the first round of review with only minor changes requested. With this experiment, we showed that the current peer-review system, already burdened by the overwhelming increase in number of publications, might be not ready to also handle the explosion of these techniques, showing the urgent need for the entire community to address both the issue of generative AI in scientific literature and probably a more profound discussion on the entire peer-review process. CLINICAL RELEVANCE STATEMENT: Generative AI models are shown to be able to create a full manuscript without any human intervention that can survive peer-review. Given the explosion of these techniques, a profound discussion on the entire peer-review process by the scientific community is mandatory. KEY POINTS: • The scientific community has raised several concerns about a possible fraudulent use of AI in scientific literature. • We asked a generative AI program to write a technical report on a non-existing technique, receiving in return a full technically sound report, substantiated by equations and references, that passed peer-review. • This experiment showed that the current peer-review system might be not ready to handle the explosion of generative AI techniques, advising for a profound discussion on the entire peer-review process.
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