Creation of Scientific Response Documents for Addressing Product Medical Information Inquiries: Mixed Method Approach Using Artificial Intelligence
This study establishes a framework for integrating LLM and machine learning into SRD development, supported by a pharmaceutical company survey emphasizing the challenges of paraphrasing content. While machine learning models show potential for section identification and content usability assessment in data extraction and abstraction, further optimization and research are essential before full-scale industry implementation. The working group's insights guide an AI-driven content analysis; address limitations; and advance efficient, precise, and responsive frameworks to assist with pharmaceutical SRD development.
Authors
- Sandeep Gantotti (ORCID: https://orcid.org/0000-0002-3091-0086)
- Shivani Bisht (ORCID: https://orcid.org/0009-0008-4360-8485)
- Evelyn R. Hermes‐DeSantis (ORCID: https://orcid.org/0000-0001-5658-3899)
- Robert F. Horton (ORCID: https://orcid.org/0000-0001-7305-354X)
- Jerry Lau (ORCID: https://orcid.org/0009-0005-1756-785X)
- Annamaria Crisan (ORCID: https://orcid.org/0009-0001-9218-354X)
- J. H. Jones (ORCID: https://orcid.org/0009-0002-2786-3837)
Institutions
- Rutgers, The State University of New Jersey (US)
- Exactech (United States) (US)
- Eli Lilly (India) (IN)
- Western Laboratories (United States) (US)
- Indo Korea Science and Technology (IN)
- Pfizer (Canada) (CA)
- Merck Serono S.p.A. (Italy) (IT)
Publication Details
- Journal
- JMIR AI
- Published
- 2024-12-31
- DOI
- https://doi.org/10.2196/55277
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00