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Related Experiment Video

Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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GrantCheck-an AI Solution for Guiding Grant Language to New Policy Requirements: Development Study.

Qiming Shi1, Asil Oztekin2, George Matthew3

  • 1Center for Clinical and Translational Science, UMass Chan Medical School, Worcester, MA, United States.

JMIR Formative Research
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

A new AI tool, GrantCheck, helps researchers comply with changing grant writing policies by detecting and suggesting alternatives for sensitive language. This ensures research continuity and protects public health advancements.

Keywords:
artificial intelligencegrant applicationslarge language modelnatural language processingresearch complianceusability evaluationuser-computer interface

Related Experiment Videos

Last Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

994

Area of Science:

  • Health Policy
  • Biomedical Research
  • Artificial Intelligence in Research

Background:

  • Academic institutions face grant writing challenges due to evolving language policies.
  • Manual review is labor-intensive and can delay critical research submissions.
  • Need for scalable, secure solutions to ensure compliance without compromising scientific integrity.

Purpose of the Study:

  • Develop a secure, AI-powered tool to aid researchers in grant writing.
  • Ensure grant proposals align with current federal and state policy requirements.
  • Maintain scientific integrity and inclusivity during the grant writing process.

Main Methods:

  • GrantCheck developed on a private AWS virtual private cloud.
  • Integrated rule-based NLP with large language models via Amazon Bedrock.
  • Hybrid pipeline detects flagged terms, suggests alternatives, and includes validation to prevent hallucinations.
  • Assessed usability using the System Usability Scale.

Main Results:

  • GrantCheck achieved high performance (F1=0.84) in detecting sensitive terms, outperforming general-purpose models (e.g., GPT-4o F1=0.43, Gemini 2.5 Pro F1=0.72).
  • Usability testing with 25 faculty/staff yielded a high mean System Usability Scale score of 85.9 (SD 13.4).
  • Demonstrated strong user satisfaction and workflow integration.

Conclusions:

  • Institutionally hosted AI systems can support compliant, researcher-friendly grant writing.
  • Safeguards public health research continuity by minimizing delays and funding losses from policy changes.
  • Protects the research pipeline advancing biomedical discovery and health equity, especially in sensitive research areas.