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AI-powered digital innovations in pharmaceuticals research & development: Current landscape and case examples
Xiao Li1, Jian Dai2, Herbert Pang2
1Computational Science and Informatics, Roche Diagnostics Solutions, Santa Clara, CA, USA.
Journal of Biopharmaceutical Statistics
|May 19, 2026
Summary
Artificial intelligence (AI) is transforming pharmaceutical R&D. This paper explores AI trends, including Large Language Models (LLMs) and spatial omics, offering insights for statisticians and data scientists.
Area of Science:
- Pharmaceutical industry
- Computational biology
- Data science
Background:
- AI and machine learning (ML) are increasingly vital in pharmaceutical research and development.
- Emerging AI technologies like Large Language Models (LLMs) and spatial omics present new opportunities.
- Understanding current AI trends is crucial for statisticians and data scientists in the pharmaceutical sector.
Purpose of the Study:
- To examine current trends in AI-powered digital innovations in the pharmaceutical industry.
- To focus on the challenges and opportunities presented by LLMs, Generative AI (GenAI), and ML for spatial omics.
- To provide an overview for integrating AI into pharmaceutical R&D and inspire future applications.
Main Methods:
- Review of recent applications of AI technologies by leading pharmaceutical companies.
- Analysis of challenges and opportunities associated with AI adoption.
- Inclusion of case examples illustrating clinical and operational utilities of AI.
- Brief mention of regulatory guidance on AI-driven tools in drugs and devices.
Main Results:
- AI adoption in pharmaceuticals offers significant potential benefits alongside inherent challenges.
- Case examples demonstrate AI's utility in high-plex tissue image analysis for single-cell segmentation and spatial pattern discovery.
- Retrieval Augmented Generation (RAG) using LLMs shows promise for standardizing clinical trial monitoring.
Conclusions:
- AI integration, particularly LLMs and spatial omics, is reshaping pharmaceutical R&D.
- Statisticians and data scientists can leverage these insights for current R&D and future AI applications.
- The paper provides a valuable overview for navigating AI advancements in the pharmaceutical landscape.
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