Magnus Gray Data-verified
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Biography and Research Information
OverviewAI-generated summary
Magnus Gray's research focuses on the application of artificial intelligence (AI) and natural language processing (NLP) within regulatory science, particularly concerning drug labeling and the identification and mitigation of bias in AI models. He has investigated methods for integrating large language models (LLMs) into regulatory environments to enhance transparency and trustworthiness, using drug labeling documents as a case study. Gray's work includes developing frameworks for classifying free text in regulatory documents and benchmarking bias in healthcare AI models, employing techniques such as SD-WEAT to measure bias across sensitive populations. His research also explores the comparative performance of molecular descriptors and AI-based embeddings for toxicity prediction. Gray has published extensively on these topics, with a recent focus on bias assessment in language embedding models and the application of NLP to drug labeling text mining. His scholarship metrics include an h-index of 6, with 14 total publications and 78 citations. He collaborates with researchers from the National Center for Toxicological Research, including Leihong Wu and Joshua Xu, and Mariofanna Milanova from the University of Arkansas at Little Rock.
Metrics
- h-index: 6
- Publications: 14
- Citations: 83
Selected Publications
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Comparative Study of Molecular Descriptors and AI-Based Embeddings for Toxicity Prediction (2025)
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Benchmarking bias in embeddings of healthcare AI models: using SD-WEAT for detection and measurement across sensitive populations (2025)
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Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods (2024)
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SD-WEAT: Towards Robustly Measuring Bias in Input Embeddings (2024)
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A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document (2024)
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RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling (2023)
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Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science (2023)
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Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents (2023)
Collaboration Network
Top Collaborators
- Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents
- Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods
Showing 5 of 8 shared publications
- Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents
- Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents
- Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science
- Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science
- Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods
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