Note: OpenAI leads the general AI space, but AI companies are developing deep research tools and experimenting with AI-powered academic searching in support of research. Perhaps you have faculty or students asking you to present these tools to classes.
"...Undermind AI stood out ...as it offered unique features that could aid students and researchers in conducting reviews; [it] will ask clarifying questions that are designed to strengthen your research questions...[and] generate a new research question based on your answers....if used properly, Undermind AI can be a very helpful tool for research." — Patterson et al, 2025.
Undermind.ai is an AI-powered research tool designed to "condense the research process from weeks to minutes" and to improve searching, particularly complex scientific searching and synthesis. UM uses AI to mimic a human researcher's iterative search process, analyzing results in stages and dynamically adjusting the search based on what it learns. This approach aims to deliver more relevant and comprehensive results than traditional search engines. Undermind.ai uses deep research technology, and positions itself as an AI-powered research assistant that conducts thorough literature searches with a focus on scientific discovery.
In a study conducted by Hunter, Booth et al (2025), the researchers said, "...by demonstrating the application of tools such as Scite and Undermind, this case study shows how AI can support targeted, conceptually driven searches—enabling researchers to access rich, relevant data with greater efficiency. However, as with any tool, their value lies in how they are used. This is especially true for AI-powered searches, which are still relatively new. We are continuing to learn how best to use these tools effectively, and their role is to complement—not replace—the judgment and interpretive thinking of the researcher."Not to mention information retrievalists and librarians!
Here's a more detailed breakdown of Undermind's features:
AI-powered research assistant: a chatbot acts as research assistant, or co-pilot, helping users to focus their information need and find relevant information by intelligently navigating a search done in Semantic Scholar.
Iterative search process: Instead of relying on a single query, Undermind analyzes results in stages, using language models to identify key papers, themes, and potential search refinements.
Focus on complex topics: designed to handle complex research questions where a single keyword search might not be sufficient.
Mimics human researchers: built to emulate the way a human researcher would approach a literature review, by examining results in stages, refining the search based on initial findings, and exploring connections between different papers.
Features: Undermind offers features like brainstorming with a copilot, deep searches across titles, abstracts, and full texts (where available), LLM-generated summaries, and organized reports with visualizations like citation networks.
Benefits: UM aims to save researchers considerable time and effort by providing more targeted and comprehensive results than traditional search engines, potentially 10-50 times better, according to its creators, than Google Scholar.
Many (if not all) of the AI-powered search tools such as Undermind.ai use retrieval augmented generation (RAG) to deliver results. RAG refers to a technique combining the strengths of retrieval-based and generative AI models. In RAG, an AI system first retrieves information from a large dataset or knowledge base and then uses this retrieved data to generate a response or output. Essentially, the RAG model augments the generation process with additional context or information pulled from relevant sources.
Presentation (short) showing Undermind.ai
Note: This presentation was selected by a librarian due to its brevity in conveying information about the product. As this is a marketing video and tutorial, some of the claims of the video should be tested and verified.
Undermind.ai is a highly useful tool and researchers will benefit from using it to start their literature reviews. For health sciences librarians, it offers an AI-powered way to start a literature review in biomedicine, and to find highly relevant (seed) papers in support of knowledge syntheses. The system’s latency or response time (from 8 to 10 minutes) limits its utility in some contexts, despite helpful summaries, match scores and a final report. However, when Undermind reports, for example, that search completeness is between 87% to 100% (in locating all relevant papers), the cost of the subscription to replace the creation of an initial search and screening of papers is worth the investment. Similar to Elicit.com, Undermind is a sophisticated option for researchers, and represents the future direction of AI search tools. Users should weigh buying a subscription and the platform’s shortcomings against the time savings offered by the final report. Note any information provided to you on this page is changing, so check the tool's website for the current information (or discuss with a librarian). Incidentally, I like to make a distinction between searching for sources and searching for answers. This much is true: LLMs provide the second while hiding the first.
Note: Please use your critical reading skills while reading entries. No warranties, implied or actual, are granted for any health or medical search or AI information obtained while using these pages. Check with your librarian for more contextual, accurate information.
