Artificial Intelligence and Research Impact Analysis Roundtable Discussion

This two-hour virtual roundtable discussion will explore the evolving role of artificial intelligence (AI) in research impact analysis. The program will begin with brief presentations by our panelists, followed by an open discussion. Research impact analysis uses quantitative and qualitative approaches to examine publications, citations, collaboration networks, and other indicators of scientific contribution. With the rapid advancement of generative AI and machine learning tools, new methods are emerging to enhance content analysis, trend identification, visualization, and interpretation of research outputs.  

By the end of this training, attendees will be able to: 

• Describe how AI-driven techniques can enhance research impact analysis in the biomedical field 

• Identify emerging AI tools and methods for citation, content, and trend analysis 

• Provide examples of how AI-informed research impact analysis can support planning, evaluation, and reporting at NIH  

Attendees are not expected to have any prior knowledge of research impact analysis techniques or tools.  

Presenters:

• Joelle Mornini, NIH Library 
  Using ChatGPT to Create Visualizations
• Troy Zarcone, NIGMS  
  Surviving the AI Bubble: Staying on the Cutting Edge While Avoiding the Bleeding Edge
• Comfort Kai, OD 
  Lauren Oliveira Hashiguchi, NINR 
  Esther Yui, NINR  
  Research to Insights: Prompting AI to Summarize Impact
• Hua Ou, MD, Ph.D., OD 
  Lessons from Using Local LLMs (ChIRP) for Annual Portfolio Analysis
• James McClain, OD 
  Evan Ochsenfaber, OD 
  Overview of the All of Us Research Program's LLM Pipeline to Automatically Ingest, Authenticate, Categorize, and Synthesize Journal Publications by Researchers
• Vanessa Barnes, M.S., OD  
  More Than MeSH: AI-Powered Topic Mapping of Publications from the Kids First Program