AI in scientific publishing: Slower, worse, and more expensive.

Clinical Snapshot

Key Findings

• P Value: Not applicable — opinion piece with no hypothesis testing

• Effect Size: Not reported — no quantitative effect estimates are provided

• Primary Outcome: The editorial asserts that AI integration in scientific publishing has resulted in slower manuscript processing, degraded quality of the scientific record, and increased operational costs due to the need for rigorous human verification of AI-generated content

• Nnt Or Sensitivity: Not applicable — no diagnostic, therapeutic, or prognostic data presented

• Confidence Interval: Not reported — no statistical analysis conducted

Clinical Application

The editorial's implicit recommendation — that publishers should invest in rigorous human oversight of AI-generated content — is feasible in principle but resource-intensive. Implementation would require editorial workforce expansion, updated submission policies, and potentially new technological tools for AI-content detection, all of which carry significant cost and capacity implications for smaller journals and learned societies. In the Australian context, this editorial has relevance for the NHMRC and ARC research integrity frameworks, which are increasingly grappling with AI disclosure requirements in grant applications and publications. The Australian Research Integrity Committee (ARIC) and institutional Research Integrity Advisors will need to consider how AI-generated content in manuscripts intersects with authorship standards under the Australian Code for the Responsible Conduct of Research (2018). The Medical Journal of Australia and other domestic journals are navigating similar editorial policy questions. The TGA's regulatory submissions and PBS economic evaluations, which rely on published evidence, are indirectly affected if AI-generated research of uncertain quality enters the literature base. RACGP clinical guidelines development processes, which synthesise published evidence, would similarly be vulnerable to integrity failures in the upstream literature. Scientific editors, journal publishers, research institutions, funding bodies, and researchers who submit to or rely upon peer-reviewed scientific literature — including clinicians who use published evidence to inform practice

Abstract

There's a saying in the management world, popularized by NASA administrator Daniel Goldin in the 1990s, that the goal of technological improvements is to make products faster, better, and cheaper. Although this strategy had some success in the aerospace industry, the zealots of artificial intelligence (AI) have been making the same argument regarding how it will transform work, claiming that so little human effort will be required that humanity will enter an era of radical abundance, free from disease, drudgery, and danger, among other benefits, leaving society with more time for creative pursuits. But history tells a different story. When machines began to increase productivity during the second industrial revolution, American engineer Frederick Winslow Taylor's The Principles of Scientific Management encouraged corporations to use surveillance to get employees to work harder and longer, an approach that exhausted and discouraged workers and led to the transfer of knowledge and any decision-making from workers to management, while enriching the profits for only those at the top. Yet, it remains foundational to the American economic enterprise. Indeed, scientific publishing is starting to experience some Taylorism with the insertion of AI. Rigorous human checking of AI-generated research papers is creating bottlenecks as publishers strive to maintain the integrity of the scientific record. The challenge is requiring even more human effort, making the whole endeavor slower and more expensive.

References

1. 1.Thorp, H. H. (2026). AI in scientific publishing: Slower, worse, and more expensive. Science, 383. https://doi.org/10.1126/science.aek5570