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The rise of intelligent research: how should artificial intelligence be assisting researchers in conducting medical literature searches?
Shankargouda Patila, Marcos Roberto Tovani-Paloneb,
Corresponding author
marcos_palone@hotmail.com

Corresponding author.
a College of Dental Medicine, Roseman University of Health Sciences, South Jordan, Utah 84095, USA
b Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu 600077, India
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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="para0001" class="elsevierStylePara elsevierViewall">Human ingenuity has drawn Artificial Intelligence &#40;AI&#41; from the realms of science fiction to reality&#46;<a class="elsevierStyleCrossRef" href="#bib0001"><span class="elsevierStyleSup">1</span></a> Three of the biggest tech conglomerates in the world have recently announced the release of AI models that may reinvent how academic researchers conduct their searches and literature reviews&#46;<a class="elsevierStyleCrossRef" href="#bib0002"><span class="elsevierStyleSup">2</span></a> With Microsoft integrating AI into its search engine&#44; the once futuristic idea of intelligent search has become a tangible reality&#46; These cutting-edge AI systems&#44; coupled with massive search engines&#44; have harnessed the vast ocean of information and organized it in ways that were once thought impossible&#46; Researchers are no longer forced to navigate the treacherous waters of irrelevant information with no compass to guide them&#46; Scholarly search has been transformed into a voyage of discovery&#44; with the AI system adapting to the researcher&#39;s needs in real time&#46;</p><p id="para0002" class="elsevierStylePara elsevierViewall">Google Scholar is the most widely used search engine&#44; enabling researchers&#44; academicians&#44; and students to access a vast pool of information related to their area of study&#46; These search engines rely on algorithms to index and rank billions of web pages and provide relevant results to users based on keyword matching and relevance ranking&#46; Traditional literature search often involves typing in a search string and parsing through the results&#46; Researchers have to trek through a wide range of academic sources&#44; including peer-reviewed journals&#44; conference proceedings&#44; and academic publishers&#44; and examine the search results based on their relevance and significance&#46;</p><p id="para0003" class="elsevierStylePara elsevierViewall">AI can be used to perform such cumbersome&#44; repetitive tasks&#44; freeing human capital to work on higher-impact problems&#46; It can process more information more quickly than a human&#44; finding patterns and discovering relationships in data that a human researcher may miss&#46;<a class="elsevierStyleCrossRef" href="#bib0003"><span class="elsevierStyleSup">3</span></a> By leveraging their ability to process and analyze large amounts of text-based data&#44; Large Language Models &#40;LLMs&#41; have the potential to serve as an effective tool for researchers&#46;<a class="elsevierStyleCrossRef" href="#bib0004"><span class="elsevierStyleSup">4</span></a> LLMs correspond to AI systems that have been trained to generate and manipulate text&#46; LLMs are trained on massive amounts of data&#44; typically drawn from the internet&#44; books&#44; and other written materials to learn patterns and relationships within the data&#46; These models&#44; based on deep neural networks&#44; are capable of producing coherent and semantically meaningful text that is indistinguishable from text written by humans&#46; They can assist in retrieving relevant literature by utilizing their vast understanding of language to provide search results that are more precise and relevant than traditional keyword-based search engines&#44; serving up information in clear simple sentences rather than as a pile of internet links that need to be explored further&#46;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">5</span></a></p><p id="para0004" class="elsevierStylePara elsevierViewall">An LLM integrated with a search engine analyses a scholarly literature search query and understands the context of the search&#44; such as the author&#44; publication year&#44; and research area&#44; allowing it to provide more accurate and relevant results&#44; cutting down the time spent on manual literature searches&#46; They can provide explanations and additional information in response to follow-up questions&#44; allowing researchers to quickly locate relevant information&#46;<a class="elsevierStyleCrossRef" href="#bib0006"><span class="elsevierStyleSup">6</span></a> This level of interactivity is not possible with traditional search engines&#46;</p><p id="para0005" class="elsevierStylePara elsevierViewall">A researcher exploring an unfamiliar topic can use a language model to process a large corpus of papers and extract the keywords and named entities&#44; and then use these terms as the basis for a query&#46; The algorithms can analyze the citations in various papers and identify emerging trends&#44; highlighting papers that are particularly relevant&#46; This can significantly speed up the literature review process&#44; generating pertinent search results that match the researcher&#39;s specific needs&#44; and saving time and effort&#46;</p><p id="para0006" class="elsevierStylePara elsevierViewall">LLMs can also be used to perform summarization tasks&#44; which can assist researchers in quickly reviewing the contents of large numbers of papers and rapidly assessing their relevance to their research inquiry&#46; In this context&#44; AI systems are able to generate summaries and extract and categorize relevant information &#40;findings&#44; numerical data&#44; text data&#44; image data&#41; from papers&#44; which can provide researchers with a concise overview of their contents&#46; The extracted data can be used to generate insights and inform analysis&#46; Such apps powered by AI&#44; such as Paper Digest and Penelope&#46;ai&#44; are already helping academics get their research published faster&#46;</p><p id="para0007" class="elsevierStylePara elsevierViewall">However&#44; it is important to note that AI language models are not perfect and may make errors or inaccuracies in their predictions&#46; These factual errors can have far-reaching consequences&#44; as Google&#39;s parent company Alphabet found to its dismay&#46; Its flagship AI-language model&#44; titled &#39;Bard&#44;&#39; made an elementary factual error in its demonstration video&#44; causing the company&#39;s shares to drop approximately&#160;100&#160;billion dollars in market value&#46; These errors are largely due to faulty training data used to train the model&#46; If the training data contains inaccuracies or biases&#44; these may be incorporated into the model&#39;s predictions&#44; leading to errors&#46; For example&#44; if a language model is trained on news articles that contain inaccuracies&#44; it may report those inaccuracies in its predictions&#46;</p><p id="para0008" class="elsevierStylePara elsevierViewall">Moreover&#44; the sheer size and complexity of language models can also contribute to errors&#46; As language models become larger and more complex&#44; it becomes increasingly difficult to evaluate and validate their predictions thoroughly and ensure that they make accurate and reliable inferences&#46; A researcher using AI-powered search engines must exercise due diligence by thoroughly verifying the veracity and accuracy of all obtained data prior to utilization&#46;</p><p id="para0009" class="elsevierStylePara elsevierViewall">In short&#44; the power of LLMs has been rising and their impact on academic research is set to become more profound&#46; By leveraging the power of AI&#44; search engines can now provide results that go beyond simple keyword matches and instead take into account the meaning and context of the search query&#46; This combination can help to simplify many of the complex tasks involved in scholarly research&#44; allowing researchers to focus on the core objectives of their work and achieve their research goals more effectively&#46; Despite this&#44; caution is needed&#46; If on the one hand&#44; the possibilities are almost endless&#44; on the other&#44; all inherent limitations must also be taken into account&#46; In light of this&#44; human innovation and critical thinking should not be replaced as a whole&#44; given that many of the inconsistencies and perceptions of the real world are not plausible to be managed by machines or new technologies&#44; such as AI&#46;<a class="elsevierStyleCrossRef" href="#bib0007"><span class="elsevierStyleSup">7</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a></p></span>"
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Article information
ISSN: 18075932
Original language: English
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es en pt

¿Es usted profesional sanitario apto para prescribir o dispensar medicamentos?

Are you a health professional able to prescribe or dispense drugs?

Você é um profissional de saúde habilitado a prescrever ou dispensar medicamentos