AI Tools in Research

The real-world applications of AI in research can significantly impact evidence synthesis. This blog will delve into different AI tools and their specific applications. We will explore how these tools can directly streamline research for healthcare decisions, making research teams' work more efficient and effective.

ChatGPT, a generative AI tool developed by OpenAI, has potential applications in various fields. In a recent webinar, Evidence Prime discussed the possibility of ChatGPT replacing tools for systematic reviews. While it's clear that ChatGPT won't replace these tools, it can certainly streamline some of the research tasks. By serving as a sounding board, ChatGPT can assist researchers in drafting the initial framework of their projects, such as generating PICO questions. However, it's crucial to remember that generative AI models like ChatGPT can make assumptions and may produce inaccurate answers. Therefore, researchers can use ChatGPT as a source of inspiration and always fact-check its outputs to ensure accuracy and reliability, underscoring the importance of human oversight in AI use.

Consensus uses AI to expedite research by providing insights from over 200 million research papers. With tools like the Consensus Meter and Copilot, users can quickly grasp the direction of current findings and identify the most relevant papers. The platform is designed for students, researchers, clinicians, and anyone seeking evidence-based answers. It emphasises transparency, with all results directly linked to the underlying research papers, ensuring reliability and depth.

Laser AI streamlines the systematic review process with its advanced AI capabilities tailored for Living Systematic Reviews. This tool optimises the entire research process with AI-supported screening and data extraction functionalities. Researchers benefit from faster screening with AI models that support the title & abstract and data extraction processes. Collaboration is simplified with real-time team interactions and reusable project templates, fostering consistency and productivity across research projects. Laser AI empowers researcher teams in healthcare by saving time, improving accuracy, and facilitating transparent, audit-ready systematic reviews essential for evidence-based decision-making.

Scite is a powerful AI research partner, leveraging advanced language models (LLMs) to enhance research workflows with insightful answers and precise control. Researchers can pose specific questions and receive detailed responses illuminating the AI's underlying thought process. Scite's customisable settings enable users to refine searches by year ranges, topics, publication types, or preferred journals, ensuring relevance and accuracy in retrieving scholarly papers. Additionally, researchers have the flexibility to guide Scite's decision-making process, including the choice to incorporate or override references as needed.

Semantic Scholar is an AI-driven platform that accelerates scientific breakthroughs through advanced search and discovery tools. Developed by the Allen Institute for AI, Semantic Scholar offers free access to over 200 million academic papers from various publishers and data providers. Its state-of-the-art AI models analyse and classify papers, enabling researchers to glean insights and connections that facilitate more profound understanding and discovery in their fields. Semantic Scholar emphasises equal access to scientific knowledge, supporting open access initiatives and providing datasets like the Semantic Scholar Academic Graph (S2AG) and Open Research Corpus (S2ORC). These resources empower the global research community to leverage AI for innovative research, enhancing collaboration and advancing scientific progress across disciplines.

Connected Papers offers a dynamic visual tool for exploring academic research through interconnected graphs. Leveraging the Semantic Scholar database, which encompasses a vast database of papers across various scientific fields, Connected Papers allows researchers to input paper identifiers, keywords, or DOIs to generate visual graphs of related papers. This interactive approach gives researchers a comprehensive overview of a particular academic field, highlighting trends, influential works, and the relationships between papers. Users can navigate through these visual representations to identify pivotal papers, discover new connections, and explore the evolution of research topics over time.

Perplexity offers a fast solution to research and collect information for your systematic reviews or related projects. Similar to other AI tools, users can ask the tool questions to leverage the integration with Copilot to further clarify their questions and, eventually, the results. Depending on the filters, search results can yield information from various sources, from the web to academic journals. From the search results, users can further change and specify their questions to find the perfect results. 

These technologies are indispensable in modern medical research, from streamlining systematic reviews with Laser AI to enhancing research discoveries with Semantic Scholar. The potential benefits of AI in research are immense. As the field evolves, staying informed about the latest tools and their applications will benefit healthcare professionals and researchers.

‍