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This study introduces a framework to prioritize Artificial Intelligence (AI) initiatives in the life sciences sector. It helps professionals evaluate AI applications across the product lifecycle and operational domains for strategic decision-making.

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Area of Science:

  • Life Sciences
  • Biotechnology
  • Pharmaceuticals

Background:

  • Artificial Intelligence (AI) adoption is rapidly increasing in the life sciences.
  • Opportunities exist to improve biopharmaceutical research, development, and commercialization.
  • The sector needs structured tools for prioritizing AI initiatives.

Purpose of the Study:

  • To present a framework for categorizing and evaluating potential AI applications in the life sciences.
  • To organize AI use cases by product lifecycle phase and operational domain.

Main Methods:

  • A structured mixed-methods approach was used.
  • This included a modified Delphi consensus process with industry experts.
  • Qualitative case study review and iterative framework refinement were also employed.

Main Results:

  • A matrix framework was developed to assess AI opportunities across the product lifecycle (research, clinical development, commercialization, post-marketing).
  • AI's pervasive impact, data-driven strategies, and regulatory/ethical challenges were highlighted.
  • The framework aids in evaluating AI applications in biopharma.

Conclusions:

  • The framework offers a practical model for strategic AI adoption in the life sciences.
  • It supports decision-making for AI integration in biopharmaceutical companies.
  • This work provides a foundation for future research and transformation efforts.