Problem
Research questions and friction points this paper is trying to address.
artificial intelligence
NIH-funded research
clinical deployment gap
health disparities
research portfolio analysis
An Analysis of Artificial Intelligence Adoption in NIH-Funded Research
🤖 AI Summary
This study addresses the critical gap in systematic understanding of artificial intelligence (AI) adoption within National Institutes of Health (NIH)-funded biomedical research, which hinders effective science policy and health equity strategies. To this end, the authors introduce a novel large-scale semantic analysis framework that integrates large language models with human-in-the-loop validation, applied to 58,746 NIH-funded projects. The analysis reveals that 15.9% of projects are AI-related and receive a 13.4% funding premium; however, 79% remain confined to the research and development phase, with only 14.7% advancing to clinical deployment. Notably, just 5.7% of AI projects explicitly address health disparities, highlighting a pronounced strategic imbalance and a significant translational gap between AI innovation and equitable health impact.
📝 Abstract
Understanding the landscape of artificial intelligence (AI) and machine learning (ML) adoption across the National Institutes of Health (NIH) portfolio is critical for research funding strategy, institutional planning, and health policy. The advent of large language models (LLMs) has fundamentally transformed research landscape analysis, enabling researchers to perform large-scale semantic extraction from thousands of unstructured research documents. In this paper, we illustrate a human-in-the-loop research methodology for LLMs to automatically classify and summarize research descriptions at scale. Using our methodology, we present a comprehensive analysis of 58,746 NIH-funded biomedical research projects from 2025. We show that: (1) AI constitutes 15.9% of the NIH portfolio with a 13.4% funding premium, concentrated in discovery, prediction, and data integration across disease domains; (2) a critical research-to-deployment gap exists, with 79% of AI projects remaining in research/development stages while only 14.7% engage in clinical deployment or implementation; and (3) health disparities research is severely underrepresented at just 5.7% of AI-funded work despite its importance to NIH's equity mission. These findings establish a framework for evidence-based policy interventions to align the NIH AI portfolio with health equity goals and strategic research priorities.
Innovation
Methods, ideas, or system contributions that make the work stand out.
large language models
human-in-the-loop
semantic extraction
AI adoption analysis
research portfolio analysis
🔎 Similar Papers
No similar papers found.
