{"id":149002,"date":"2025-11-19T22:07:16","date_gmt":"2025-11-19T22:07:16","guid":{"rendered":"https:\/\/www.newsbeep.com\/ie\/149002\/"},"modified":"2025-11-19T22:07:16","modified_gmt":"2025-11-19T22:07:16","slug":"googles-new-scholar-labs-search-uses-ai-to-find-relevant-studies","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ie\/149002\/","title":{"rendered":"Google\u2019s new Scholar Labs search uses AI to find relevant studies"},"content":{"rendered":"

Google has announced<\/a> it\u2019s testing a new AI-powered search tool, Scholar Labs<\/a>, that\u2019s designed to answer detailed research questions. But its demonstration highlighted a bigger question about finding \u201cgood\u201d science studies. How much will scientists trust a tool that forgoes typical ways of gauging a study\u2019s popularity with the scientific establishment in favor of reading the relationships between words to help surface good research?<\/p>\n

The new search tool uses AI to identify the main topics and relationships in a user\u2019s query and is currently available to a limited set of logged-in users. The demo video from Scholar Labs featured a question about brain-computer interfaces (BCIs). I have a PhD in BCIs, so I was eager to see what Scholar Labs pulled up.<\/p>\n

The first result was a review paper of BCI research published in 2024 in a journal called Applied Sciences. Scholar Labs includes explanations for why the results matched the query, so it pointed out that the paper discusses research into a noninvasive signal called electroencephalogram and surveys some leading algorithms in the field.<\/p>\n

\"Scholar<\/a><\/p>\n

Scholar Labs uses AI to surface science papers that Google says best match the user\u2019s research question. Screenshot: Google Scholar Labs<\/p>\n

But I noticed that Scholar Labs lacks the filters for common metrics used to separate \u201cgood\u201d studies from \u201cnot-so-good\u201d ones. One metric is the number of times that a study has been cited by other studies since its publication, which loosely translates to a paper\u2019s popularity. It\u2019s also associated with time: A recently published study might have zero citations or rack up hundreds within a few months; a study from the \u201990s may tout thousands. Another metric is the \u201cimpact factor\u201d of a science journal. Journals that publish widely cited studies have a higher impact factor and thus have a reputation for being more rigorous or meaningful to the scientific community. Applied Sciences self-reports an impact factor of 2.5<\/a>. Nature, for comparison, says its impact factor is 48.5<\/a>.<\/p>\n

The original Google Scholar has an option for ranking studies by \u201crelevancy\u201d and lists the number of citations for each result. The goal of the new Scholar Labs is to dig up \u201cthe most useful papers for the user\u2019s research quest,\u201d Google spokesperson Lisa Oguike told The Verge It does so by ranking papers in the same way as the researchers themselves, Google says, by \u201cweighing the full text of each document, where it was published, who it was written by, as well as how often and how recently it has been cited in other scholarly literature.\u201d<\/p>\n

However, the new Scholar Labs will not sort or limit results based on a paper\u2019s citation count or a journal\u2019s impact factor, Oguike told The Verge.<\/p>\n

\"Google<\/a><\/p>\n

Image: Google Scholar<\/p>\n

\u201cImpact factors and citation counts depend on the research area of the papers and it can be hard for most users to guess suitable values in the context of specific research questions,\u201d Oguike wrote. \u201cLimiting by impact factor or citation counts can often miss key papers — in particular, papers in interdisciplinary\/adjacent fields\/journal or recently published articles,\u201d Oguike added.<\/p>\n

Metrics like citation count and impact factor are \u201cpretty coarse assessments of a paper\u2019s quality,\u201d associate professor of neurology at Vanderbilt University Medical Center Matthew Schrag said in an interview with The Verge, agreeing with Google\u2019s statement. They \u201cspeak more about the social context of the paper\u201d rather than its quality, although \u201cthose two things hopefully are correlated,\u201d he said.<\/p>\n

Schrag, who researches Alzheimer\u2019s disease, is one of the many scientists-sleuths who have flagged dubious data in published science studies<\/a>. The efforts of data sleuths like Schrag, and a closer attention by the science community at large, have resulted in studies pulled from well-regarded journals because of doctored images<\/a>, corrections issued by Nobel Prize winners<\/a>, and federal investigations<\/a> into faked data.<\/p>\n

Still, it\u2019s difficult to not use citation count or a journal\u2019s reputation to casually vet a study, especially when entering a new field. Professor of rehabilitation sciences at Tufts University, James Smoliga, a frequent user of the original Google Scholar, finds himself believing highly cited papers to be more trustworthy. \u201cI\u2019m guilty of it just like everybody else is,\u201d he said to The Verge. He does so despite having debunked<\/a> the methods used in a study with thousands of citations. \u201cAnd I know myself that\u2019s not the case but yet I still fall for that trap because what else am I going to do?\u201d<\/p>\n

I repeated the Scholar Labs demo query about BCI research for stroke patients in PubMed, a leading repository of biomedical and health research run by the US National Institutes of Health National Library of Medicine. Unlike Scholar Labs, PubMed relies extensively on filters and terms connected with ors and ands. I narrowed my results to only review articles of clinical research, meaning only done on humans, from the past five years. I excluded preprints, which are studies posted directly to a paper repository like arXiv or bioRxiv without having gone through a review process from other scientists. Two of the six results focused exclusively on electroencephalogram as the primary type of noninvasive BCI used to help stroke patients.<\/p>\n

\"Webpage<\/a><\/p>\n

PubMed allows users to filter search results by factors like time, article type, and peer-review. Screenshot: PubMed<\/p>\n

Users will be able to ask for \u201crecent\u201d papers in their query and specify a period of time in their request, and Scholar Labs uses the \u201cfull-text of research papers\u201d to find results that match the user query, Oguike added.<\/p>\n

Google is calling Scholar Labs a \u201cnew direction for us\u201d and says it plans to incorporate user feedback in the future. It has a waitlist for access.<\/p>\n

Schrag thinks AI-powered search, like that of the new Scholar Labs, has a place in the scientific ecosystem. It could, in theory, cast a wider net to surface papers that otherwise slipped through the cracks, or add additional context about a paper\u2019s popularity across social media platforms, he added. Studies need a holistic appraisal, he said, which AI might be able to address. \u201cYou have to have a sense of what the standards in the field are in terms of rigor and whether a study meets that,\u201d he added.<\/p>\n

Ultimately, scientists are responsible for determining what science is impactful, Schrag said. It requires reading and engaging with science literature \u201cto be the final arbiters and not to let algorithms be the final arbiter of what we consider high quality.\u201d<\/p>\n

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