Also: This open textbook (or wiki channel) is intended to help librarians and other information professionals learn about AI. It is not, in itself, meant to be seen as promotion of AI. If anything, the goal is harms mitigation or harms reduction.
Introduction
OpenAI leads the general AI space, especially GenAI, but many 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. (Also: this entry is intended to help librarians and other information professionals learn about AI. It is not, in itself, meant to be seen as promotion of AI.) Many tools are free with premium tiers for advanced features. For the latest developments, tools like these often update based on models from underlying providers (e.g., OpenAI, Anthropic).
Major AI Companies
Several AI companies produce GenerativeAI chatbots, and are putting major resources into AI for web searching such as:
Anthropic https://www.anthropic.com/ is an artificial intelligence research and safety-focused company that builds reliable, interpretable, and steerable AI systems designed to benefit society; known for developing a family of AI models and for emphasizing responsible AI development, including studying both the risks and opportunities of advanced AI technologies.
Google https://ai.google: 1.5 billion users monthly; develops Gemini models and Knowledge Graph; tools provide conversational, summarized search results, integrating with Google’s ecosystem, which librarians might appreciate for its broad web indexing and accessibility.
Note: Will Google Scholar survive the rise of AI-powered searching?
Microsoft https://microsoft.ai: 40 million daily users; integrates its AI assistant, Copilot, into Bing, using OpenAI’s GPT-4 for conversational search with citations. Its deep integration with Microsoft 365 is being integrated into enterprise and library knowledge management.
OpenAI https://openai.com: 1.1 billion queries daily; powers ChatGPT Search, offers conversational search with real-time web access. Librarians might test its ability to handle complex queries conversationally, though it requires a Plus subscription for advanced features.
Perplexity AI https://www.perplexity.ai/: valued at $9–18 billion; 780 million queries monthly; known for its conversational search engine using semantic search and provides cited, summarized answers. Focus on reliable sources and research-oriented interface may align with librarians’ focus but more testing is needed.
"...The idea that we should outsource academic authorship to LLMs rests on the assumption that writing is (only) a mechanical, predictable or reductive process which, with the right prompts, can be replicated with ease." — Masters, 2025.
Bottom line: For health sciences librarians, AI tools may or may not support their work with health professionals but many underlying processes for locating and processing information raise concerns for anyone interested in scientific accuracy, transparency and rigour. HSLs, like other academic librarians, are also not that enamoured of Silicon Valley broligarchies and their hegemonic power. Incidentally, librarians make a distinction between searching for sources and searching for answers. This much is true: so much of AI provide the second while hiding the first; transparency is not their strong suit. Note information provided to you on this page is changing, so check for current information (or discuss with a librarian).
Article describes how systematic reviews, long the gold standard for evaluating scientific evidence in medicine and policy, are slow and labor intensive, often taking over a year to complete. AI tools could dramatically speed up these reviews by automating tasks like screening studies and summarizing findings, potentially making evidence synthesis faster and more up-to-date. However, experts warn that many AI systems lack transparency, reproducibility, and access to complete databases, risking poor or biased results. New guidance from major evidence-synthesis organizations emphasizes cautious, responsible use of AI to preserve trustworthiness while reaping efficiency gains.
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.
