{"id":3634,"date":"2020-09-18T15:58:14","date_gmt":"2020-09-18T15:58:14","guid":{"rendered":"https:\/\/www.editage.com\/insights\/using-artificial-intelligence-ai-in-peer-review\/"},"modified":"2025-01-15T06:29:44","modified_gmt":"2025-01-15T06:29:44","slug":"using-artificial-intelligence-ai-in-peer-review","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/using-artificial-intelligence-ai-in-peer-review","title":{"rendered":"Using artificial intelligence (AI) in peer review"},"content":{"rendered":"

At first sight, it might seem that AI and the peer review process have nothing in common. As a researcher, you want your paper to be reviewed as quickly as possible. Peer reviewers are hu<\/span><\/span>man, and no machine has (yet) come up with the ability to read a paper and decide if it is worth publishing. In the run up to Peer Review Week 2020<\/a><\/b><\/span><\/span>, which focuses on the theme Trust in Peer Review, we wonder why talk about AI and peer review?\u00a0<\/span><\/span><\/span><\/span><\/span><\/p>\n

\u00a0<\/p>\n

First, let\u2019s\u00a0<\/span><\/span>clarify what kind of\u00a0AI we are talking about.\u00a0The term is used very widely to cover many different processes, some of them sophisticated, others very simple.\u00a0It is immediately obvious that, for example, the AI required to drive a car without a human driver is highly elaborate and mission-critical; you cannot afford a single error in the process without potentially causing a disaster. Yet self-driving cars depend on a high number of simpler processes, many of which use well-known, tried and tested technology. For example, the ability for cars to provide feedback to the driver when parking has been around for\u00a0many years \u2013 in fact, the\u00a0<\/span><\/span>first to be introduced<\/span><\/a><\/span><\/span><\/span><\/u>\u00a0was in 1999. It\u2019s not difficult to\u00a0<\/span><\/span>see how parking a car is dependent on\u00a0many constituent processes, one of the simplest\u00a0being able to estimate distances via a camera. The ability to park a car is built up from a number of discrete tasks of this kind.\u00a0\u00a0<\/span><\/span><\/span><\/span><\/span><\/p>\n

\u00a0<\/p>\n

Let\u2019s look now at peer review. While the crucial task of assessing a paper for novelty and innovation, may continue to be human-<\/span><\/span>centered, there are several much more mundane\u00a0operations\u00a0that the publisher has to carry out when a\u00a0manuscript\u00a0is first\u00a0received. And, clearly, there is a need to improve the current process: a\u00a0<\/span><\/span>2016 peer-review<\/span><\/span><\/span><\/a><\/span><\/span><\/span>\u00a0survey carried out by Wiley found that the rate of acceptance of invitations to review had dropped by 5% in the last five years before the survey. With fewer reviewers accepting, and more papers being published, you can see the problem in years to come.\u00a0<\/span><\/span>Is there a way\u00a0we can use AI to assist us?\u00a0\u00a0<\/span><\/span><\/span><\/span><\/span><\/p>\n

\u00a0<\/p>\n

An\u00a0<\/span><\/span>Els<\/span><\/span><\/span>evier report<\/span><\/a><\/span><\/span><\/span>\u00a0from 2018\u00a0<\/span><\/span>identified\u00a0three\u00a0tasks\u00a0in the peer review process that could use automation:\u00a0\u00a0<\/span><\/span><\/span><\/span><\/span><\/p>\n

    \n
  1. Match a manuscript with a suitable candidate reviewer<\/span><\/span>\u2019s qualifications and interest\u00a0<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n
  2. Remove candidates who have a potential conflict of interest<\/span><\/span>\u00a0<\/span><\/span><\/span><\/span><\/li>\n
  3. Look for signals that might indicate the candidate reviewer\u2019s willingness to accept a request for review.\u00a0\u00a0<\/span><\/span>\u00a0<\/span><\/span><\/span><\/span><\/li>\n<\/ol>\n

    First is the problem of identifying a relevant reviewer. That doesn’t seem so simple, certainly not for humans! Whatever the academic domain, no publisher can have a big enough staff with good knowledge of all the submitted subjects that enable them to find current experts. Paradoxically, the higher the level of education, the more specialized the knowledge, so even if a publisher hires only editors with a doctorate, their knowledge will cover only a tiny fraction of all the submitted papers. Hiring more experts, in other words, is not the answer.\u00a0\u00a0 <\/span><\/span><\/span><\/span><\/span><\/p>\n

    \u00a0<\/p>\n

    In practice, the human editor becomes an instant expert. They<\/span><\/span>\u00a0spend some time looking at names and titles of papers in the subject area to identify who has published recently in the same subject area. But this\u00a0process is slow and rather random, with no guarantee that the potential reviewers identified will have the right expertise. In fact one\u00a0survey\u00a0(\u201c<\/span><\/span>Why do Peer Reviewers decline to review<\/span><\/span><\/span><\/a><\/span><\/span><\/span>“, 2014)<\/span><\/span>\u00a0found that\u00a014% of suggestions to peer reviewers were rejected because\u00a0the reviewers approached\u00a0felt that others had more relevant subject knowledge.\u00a0That doesn\u2019t suggest a very precise\u00a0manual\u00a0selection process.\u00a0<\/span><\/span><\/span><\/span><\/span><\/p>\n

    \u00a0<\/p>\n

    Instead, <\/span><\/span>UNSILO<\/a><\/span><\/span><\/b><\/span><\/span> uses a corpus-based concept extraction tool. Its\u00a0concept engine identifies hundreds of concepts\u00a0(key phrases that distinguish each article from all the others in the corpus)\u00a0from a submission, ranks them in order of relevance to that paper, and then, for biomedical content,\u00a0matches the resulting cluster of concepts with 29 million articles and abstracts in the PubMed corpus.\u00a0Having matched articles, the system then identifies the authors of these matching papers.\u00a0Finally, it ranks the authors by relevance, and presents the top 20 for the editorial office to review.\u00a0\u00a0<\/span><\/span><\/span><\/span><\/span><\/p>\n

    \u00a0<\/p>\n

    As for conflict of interest, this is a relatively straightforward matching exercise. There are several possible types of conflict of interest (COI), but for one COI check, the system simply compares the affiliations of the manuscript author and of the proposed reviewers. If they are the same, the proposed reviewer is discarded. To carry out such a check by hand might take only a few minutes. But in a world where 3,000 new scientific articles are published every day, the saving of a few seconds for each submission is a very clear benefit.\u00a0 <\/span><\/span><\/span><\/span><\/span><\/p>\n

    \u00a0<\/p>\n

    Finally,\u00a0<\/span><\/span>the\u00a0machine carries out a few counts, and presents the results to the human editor for his or her assessment. How many papers has this potential reviewer authored? If too few, the reviewer may not have sufficient experience. If the reviewer has authored hundreds of papers, they will probably be too senior to agree to a review. So the machine can be configured, with the guidance of the human, to deliver results with the right criteria.\u00a0\u00a0<\/span><\/span><\/span><\/span><\/span><\/p>\n

    \u00a0<\/p>\n

    A key aspect of the machine component is that it does not make the final choice of reviewer \u2013 the human does that.\u00a0The machine simply states some names that match by the various\u00a0<\/span><\/span>criteria\u00a0outlined above.\u00a0In other words, the use of relatively simple checks such as these\u00a0enable the humans to do what humans do best: to form judgements.\u00a0Machines cannot\u00a0(yet)\u00a0carry out peer reviews; but, like the assisted parking tool, they can help humans use their judgement more quickly and more accurately.\u00a0\u00a0<\/span><\/span><\/span><\/span><\/span><\/p>\n

    \u00a0<\/p>\n

    About UNSILO<\/span><\/span>\u00a0<\/span><\/span><\/span><\/strong><\/p>\n

    The\u00a0<\/span><\/span>UNSILO\u00a0peer reviewer finder is just one of\u00a0the many AI-related tools now available\u00a0from\u00a0CACTUS\u00a0for the academic publishing workflow.\u00a0UNSILO<\/a>\u00a0<\/u><\/span><\/b><\/span><\/span>also\u00a0provides a range of technical checks, plus (for content discovery) a recommender engine and an automatic subject collection builder.\u00a0UNSILO also powers the continuous update of the world\u2019s largest collection of nano-related content at the Springer Nature\u00a0<\/span><\/span>nano site<\/span><\/span><\/span><\/a><\/span><\/span><\/span>.\u00a0<\/span><\/span>\u00a0<\/span><\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

    At first sight, it might seem that AI and the peer review process have nothing in common. As a researcher, you want your paper to be reviewed as quickly as possible. Peer reviewers are human, and no machine has (yet) come up with the ability to read a paper and decide if it is worth […]<\/p>\n","protected":false},"author":2019,"featured_media":33313,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2447],"tags":[2743,128,44,2742,2740],"new_categories":[],"new_tags":[],"series":[],"class_list":["post-3634","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-peer-review-week-2020","tag-ai-for-researchers","tag-journal-publishing","tag-peer-review","tag-technology-in-publishing","tag-trust-in-peer-review"],"acf":[],"yoast_head":"\nUsing AI in Peer Review | Editage Insights<\/title>\n<meta name=\"description\" content=\"At first, it seems AI and peer review have nothing in common. 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