The rise of intelligent research: how should artificial intelligence be assisting researchers in conducting medical literature searches?

Abstract

The rise of intelligent research: how should artificial intelligence be assisting researchers in conducting medical literature searches?Human ingenuity has drawn Artificial Intelligence (AI) from the realms of science fiction to reality. 1 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. 2 With Microsoft integrating AI into its search engine, the once futuristic idea of intelligent search has become a tangible reality.These cutting-edge AI systems, coupled with massive search engines, have harnessed the vast ocean of information and organized it in ways that were once thought impossible.Researchers are no longer forced to navigate the treacherous waters of irrelevant information with no compass to guide them.Scholarly search has been transformed into a voyage of discovery, with the AI system adapting to the researcher's needs in real time.Google Scholar is the most widely used search engine, enabling researchers, academicians, and students to access a vast pool of information related to their area of study.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.Traditional literature search often involves typing in a search string and parsing through the results.Researchers have to trek through a wide range of academic sources, including peer-reviewed journals, conference proceedings, and academic publishers, and examine the search results based on their relevance and significance.AI can be used to perform such cumbersome, repetitive tasks, freeing human capital to work on higher-impact problems.It can process more information more quickly than a human, finding patterns and discovering relationships in data that a human researcher may miss. 3By leveraging their ability to process and analyze large amounts of text-based data, Large Language Models (LLMs) have the potential to serve as an effective tool for researchers. 4LLMs correspond to AI systems that have been trained to generate and manipulate text.LLMs are trained on massive amounts of data, typically drawn from the internet, books, and other written materials to learn patterns and relationships within the data.These models, based on deep neural networks, are capable of producing coherent and semantically meaningful text that is indistinguishable from text written by humans.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 keywordbased search engines, serving up information in clear simple sentences rather than as a pile of internet links that need to be explored further. 5n LLM integrated with a search engine analyses a scholarly literature search query and understands the context of the search, such as the author, publication year, and research area, allowing it to provide more accurate and relevant results, cutting down the time spent on manual literature searches.They can provide explanations and additional information in response to follow-up questions, allowing researchers to quickly

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