KEY TAKEAWAYS

• The use of artificial intelligence in scientific communication is rapidly expanding, with multiple applications in manuscript preparation and editorial workflows.
• The scholarly publishing community must adapt to and embrace the use of advanced artificial intelligence.

Artificial intelligence (AI), natural language processing (NLP), and machine learning are widely employed across scholarly publishing, with a reduction in human workload a key driver of their adoption. A recent article by Dr Habeeb Razack and colleagues, published in Science Editing, examined the current and prospective impact of these technologies across the scientific publications arena. The authors concluded that greater adoption of AI in the future could increase the quality of published content as well as retrospectively improve the use of content already in the public domain.

AI is expected to play an increasingly important role in complex editorial processes and improving AI literacy among scholarly publishing stakeholders will be important for future adoption.

The article examined the use of AI across 7 areas of scholarly publishing.

1. Literature searching and information retrieval: In the current infodemic era, data handling is an increasing drain on time and resources. With more than 127,000 research papers published on COVID-19 alone, the ability of AI tools to extract data from large and noisy datasets is becoming increasingly important. AI tools can generate citation metrics, authenticate hypotheses, position results based on relevance, connect data from various domains and concept areas, access supplementary information, and automate systematic reviews.

2. Manuscript preparation: Recent improvements in NLP have further enhanced the quality of AI outputs and a number of AI-backed writing tools have entered the market. High profile examples include Grammarly and PerfectIt™.

3. Bibliography and citation management: In addition to established referencing software features, AI elements such as citation recommendations (wizdom.ai), analysis of citation quality (including identifying retractions; scite.ai), ‘SmartSearch’ algorithms (SciWheel), and tools to identify related publications (Connected Papers) can greatly reduce the time spent on referencing.

4. Target journal selection: Several web-based platforms are available to assist with journal selection. Notable examples include EndNote’s Manuscript Matcher, which uses an algorithm for determining a ‘match score’, and Elsevier’s JournalFinder, which uses a ‘fingerprint engine’ and subject specific vocabularies.

5. Plagiarism prevention: Plagiarism has long plagued scholarly publishing, but AI tools can help by identifying content similarity. This now includes novel tools that can detect plagiarism across different languages (CopyLeaks) and identify similarity in bar charts using optical character recognition. The use of AI-supported stylometry has also been suggested as a way of identifying an individual author’s writing style.

6. Peer review and quality assessment: NLP-driven AI approaches can help identify peer reviewers in a non-biased manner. Tools have also been developed to assess statistical errors (StatCheck) and quality (StatReviewer) in submitted manuscripts.

7. Editorial workflow and publication production: AI has the potential to simplify editorial tasks, including technical checks (UNSILO Evaluate) and journal-specific manuscript formatting. It can also help editors triage submissions by predicting future citation counts (Meta), and improve post-publication click and retention rates (UNSILO Recommend).

As AI use continues to expand in scholarly communication, Dr Razack and colleagues believe that advanced preparation will enhance AI utilisation and support the workforce by promoting human–machine collaboration. Although some professionals may be concerned that introduction of automated systems will lead to job losses, results of a 2019 survey suggest this is unlikely to occur.

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